├── docker ├── ingestion │ ├── requirements.txt │ ├── Dockerfile │ └── ingestion.py ├── api │ ├── requirements.txt │ ├── Dockerfile │ ├── movielens-app.py │ └── swagger │ │ └── swagger.yml └── docker-compose.yml ├── ingestion ├── test │ └── ingestion_tests.py ├── ingestion.py └── data │ └── tags.csv ├── api ├── movielens-app.py └── swagger │ └── swagger.yml └── README.md /docker/ingestion/requirements.txt: -------------------------------------------------------------------------------- 1 | py2neo==4.1.0 -------------------------------------------------------------------------------- /docker/api/requirements.txt: -------------------------------------------------------------------------------- 1 | flask==1.0.2 2 | py2neo==4.1.0 3 | connexion==1.5.3 -------------------------------------------------------------------------------- /docker/ingestion/Dockerfile: -------------------------------------------------------------------------------- 1 | # Use an official Python runtime as a parent image 2 | FROM python:3.7-slim 3 | 4 | # Set the working directory to /app 5 | WORKDIR /app 6 | 7 | # Copy the current directory contents into the container at /app 8 | ADD . /app 9 | 10 | # Install any needed packages specified in requirements.txt 11 | RUN pip install -r requirements.txt 12 | 13 | # Run ingestion.py when the container launches 14 | CMD ["python", "-u","ingestion.py"] -------------------------------------------------------------------------------- /docker/api/Dockerfile: -------------------------------------------------------------------------------- 1 | # Use an official Python runtime as a parent image 2 | FROM python:3.7-slim 3 | 4 | # Set the working directory to /app 5 | WORKDIR /app 6 | 7 | # Copy the current directory contents into the container at /app 8 | ADD . /app 9 | 10 | # Install any needed packages specified in requirements.txt 11 | RUN pip install -r requirements.txt 12 | 13 | # Make port 5000 available to the world outside this container 14 | EXPOSE 5000 15 | 16 | # Run ingestion.py when the container launches 17 | CMD ["python", "-u","movielens-app.py"] -------------------------------------------------------------------------------- /docker/docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: "3" 2 | 3 | services: 4 | 5 | # Neo4j database 6 | neo4j: 7 | image: neo4j:3.4.9 8 | ports: 9 | - 7474:7474 10 | - 7687:7687 11 | environment: 12 | NEO4J_AUTH: "none" 13 | 14 | # Python code ingesting the MovieLens dataset into Neo4j 15 | ingestion: 16 | build: ingestion 17 | depends_on: 18 | - neo4j 19 | restart: on-failure 20 | links: 21 | - neo4j 22 | environment: 23 | NEO4J_HOST: neo4j 24 | 25 | # API 26 | api: 27 | build: api 28 | ports: 29 | - 5000:5000 30 | depends_on: 31 | - neo4j 32 | restart: on-failure 33 | links: 34 | - neo4j 35 | environment: 36 | NEO4J_HOST: neo4j -------------------------------------------------------------------------------- /ingestion/test/ingestion_tests.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | from ingestion.ingestion import parseRowMovie, parseRowGenreMovieRelationships, parseRowRatingRelationships, parseRowTagRelationships, parseRowLinks 4 | 5 | class ParserTests(unittest.TestCase): 6 | 7 | # test the parsing of movies 8 | def test_parse_row_movie(self): 9 | 10 | row = "1,Toy Story (1995),Adventure|Animation|Children|Comedy|Fantasy".split(",") 11 | self.assertEquals (parseRowMovie(row), ("1", "Toy Story", "1995")) 12 | 13 | # test the parsing of genre-movie relationships 14 | def test_parse_row_genre_movie_relationship(self): 15 | 16 | row = "1,Toy Story (1995),Adventure|Animation|Children|Comedy|Fantasy".split(",") 17 | self.assertEquals (parseRowGenreMovieRelationships(row), ('1', ['Adventure', 'Animation', 'Children', 'Comedy', 'Fantasy'])) 18 | 19 | # test the parsing of rating relationships 20 | def test_parse_row_rating_relationship(self): 21 | 22 | row = "1,29,3.5,1112484676".split(",") 23 | self.assertEquals (parseRowRatingRelationships(row), ("User 1", "29", 3.5, "1112484676")) 24 | 25 | # test the parsing of tag relationships 26 | def test_parse_row_tag_relationship(self): 27 | 28 | row = "65,1617,neo-noir,1368150217".split(",") 29 | self.assertEquals (parseRowTagRelationships(row), ("User 65", "1617", "neo-noir", "1368150217")) 30 | 31 | # test the parsing of links 32 | def test_parse_row_links(self): 33 | 34 | row = "5,0113041,11862".split(",") 35 | self.assertEquals (parseRowLinks(row), ("5","0113041", "11862")) -------------------------------------------------------------------------------- /ingestion/ingestion.py: -------------------------------------------------------------------------------- 1 | import csv 2 | from py2neo import Graph, Node 3 | 4 | N_MOVIES = 1000 5 | N_RATINGS = 1000 6 | N_TAGS = 1000 7 | N_LINKS = 1000 8 | 9 | USERNAME = "neo4j" 10 | PASS = "neo4j" #default 11 | 12 | graph = Graph("bolt://localhost:7687", auth = (USERNAME, PASS)) 13 | 14 | 15 | def main(): 16 | 17 | createGenreNodes() 18 | 19 | print("Step 1 out of 4: loading movie nodes") 20 | loadMovies() 21 | 22 | print("Step 2 out of 4: loading rating relationships") 23 | loadRatings() 24 | 25 | print("Step 3 out of 4: loading tag relationships") 26 | loadTags() 27 | 28 | print("Step 4 out of 4: updating links to movie nodes") 29 | loadLinks() 30 | 31 | def createGenreNodes(): 32 | allGenres = ["Action", "Adventure", "Animation", "Children's", "Comedy", "Crime", 33 | "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", 34 | "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"] 35 | 36 | for genre in allGenres: 37 | gen = Node("Genre", name=genre) 38 | graph.create(gen) 39 | 40 | 41 | def loadMovies(): 42 | with open('data/movies.csv') as csvfile: 43 | readCSV = csv.reader(csvfile, delimiter=',') 44 | next(readCSV, None) # skip header 45 | for i, row in enumerate(readCSV): 46 | 47 | createMovieNodes(row) 48 | createGenreMovieRelationships(row) 49 | 50 | if (i % 100 == 0): 51 | print(f"{i}/{N_MOVIES} Movie nodes created") 52 | 53 | # break after N_MOVIES movies 54 | 55 | if i >= N_MOVIES: 56 | break 57 | 58 | def createMovieNodes(row): 59 | movieData = parseRowMovie(row) 60 | id = movieData[0] 61 | title = movieData[1] 62 | year = movieData[2] 63 | mov = Node("Movie", id=id, title=title, year=year) 64 | graph.create(mov) 65 | 66 | def parseRowMovie(row): 67 | id = row[0] 68 | year = row[1][-5:-1] 69 | title = row[1][:-7] 70 | 71 | return (id, title, year) 72 | 73 | 74 | def createGenreMovieRelationships(row): 75 | movieId = row[0] 76 | movieGenres = row[2].split("|") 77 | 78 | for movieGenre in movieGenres: 79 | graph.run('MATCH (g:Genre {name: {genre}}), (m:Movie {id: {movieId}}) CREATE (g)-[:IS_GENRE_OF]->(m)', 80 | genre=movieGenre, movieId=movieId) 81 | 82 | def parseRowGenreMovieRelationships(row): 83 | movieId = row[0] 84 | movieGenres = row[2].split("|") 85 | 86 | return (movieId, movieGenres) 87 | 88 | def loadRatings(): 89 | with open('data/ratings.csv') as csvfile: 90 | readCSV = csv.reader(csvfile, delimiter=',') 91 | next(readCSV, None) #skip header 92 | for i,row in enumerate(readCSV): 93 | createUserNodes(row) 94 | createRatingRelationship(row) 95 | 96 | if (i % 100 == 0): 97 | print(f"{i}/{N_RATINGS} Rating relationships created") 98 | 99 | if (i >= N_RATINGS): 100 | break 101 | 102 | def createUserNodes(row): 103 | user = Node("User", id="User " + row[0]) 104 | graph.merge(user, "User", "id") 105 | 106 | def createRatingRelationship(row): 107 | ratingData = parseRowRatingRelationships(row) 108 | 109 | graph.run( 110 | 'MATCH (u:User {id: {userId}}), (m:Movie {id: {movieId}}) CREATE (u)-[:RATED { rating: {rating}, timestamp: {timestamp} }]->(m)', 111 | userId=ratingData[0], movieId=ratingData[1], rating=ratingData[2], timestamp=ratingData[3]) 112 | 113 | def parseRowRatingRelationships(row): 114 | userId = "User " + row[0] 115 | movieId = row[1] 116 | rating = float(row[2]) 117 | timestamp = row[3] 118 | 119 | return (userId, movieId, rating, timestamp) 120 | 121 | def loadTags(): 122 | with open('data/tags.csv') as csvfile: 123 | readCSV = csv.reader(csvfile, delimiter=',') 124 | next(readCSV, None) #skip header 125 | for i,row in enumerate(readCSV): 126 | createTagRelationship(row) 127 | 128 | if (i % 100 == 0): 129 | print(f"{i}/{N_TAGS} Tag relationships created") 130 | 131 | if (i >= N_TAGS): 132 | break 133 | 134 | def createTagRelationship(row): 135 | tagData = parseRowTagRelationships(row) 136 | 137 | graph.run( 138 | 'MATCH (u:User {id: {userId}}), (m:Movie {id: {movieId}}) CREATE (u)-[:TAGGED { tag: {tag}, timestamp: {timestamp} }]->(m)', 139 | userId=tagData[0], movieId=tagData[1], tag=tagData[2], timestamp=tagData[3]) 140 | 141 | def parseRowTagRelationships(row): 142 | userId = "User " + row[0] 143 | movieId = row[1] 144 | tag = row[2] 145 | timestamp = row[3] 146 | 147 | return (userId, movieId, tag, timestamp) 148 | 149 | def loadLinks(): 150 | with open('data/links.csv') as csvfile: 151 | readCSV = csv.reader(csvfile, delimiter=',') 152 | next(readCSV, None) # skip header 153 | for i, row in enumerate(readCSV): 154 | 155 | updateMovieNodeWithLinks(row) 156 | 157 | if (i % 100 == 0): 158 | print(f"{i}/{N_LINKS} Movie nodes updated with links") 159 | 160 | # break after N_LINKS movies 161 | 162 | if i >= N_LINKS: 163 | break 164 | 165 | def updateMovieNodeWithLinks(row): 166 | linkData = parseRowLinks(row) 167 | 168 | graph.run( 169 | 'MATCH (m:Movie {id: {movieId}}) SET m += { imdbId: {imdbId} , tmdbId: {tmdbId} }', 170 | movieId=linkData[0], imdbId=linkData[1], tmdbId=linkData[2]) 171 | 172 | def parseRowLinks(row): 173 | movieId = row[0] 174 | imdbId = row[1] 175 | tmdbId = row[2] 176 | 177 | return (movieId, imdbId, tmdbId) 178 | 179 | 180 | if __name__ == '__main__': 181 | main() -------------------------------------------------------------------------------- /api/movielens-app.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, jsonify, request 2 | from py2neo import Graph, Node, NodeMatcher 3 | import connexion 4 | 5 | app = Flask(__name__) 6 | 7 | USERNAME = "neo4j" 8 | PASS = "neo4j" #default 9 | 10 | graph = Graph("bolt://localhost:7687", auth = (USERNAME, PASS)) 11 | 12 | ####### Movie ####### 13 | 14 | # Get the available deatils of a given movie 15 | @app.route('/api/movie/details/') 16 | def getMovieData(title): 17 | matcher = NodeMatcher(graph) 18 | movie = matcher.match("Movie", title={title}).first() 19 | 20 | return jsonify(movie) 21 | 22 | # Get the genres associated with a given movie 23 | @app.route('/api/movie/genres/<title>') 24 | def getMovieGenres(title): 25 | genres = graph.run('MATCH (genres)-[:IS_GENRE_OF]->(m:Movie {title: {title}}) RETURN genres', title=title) 26 | 27 | return jsonify(list(genres)) 28 | 29 | # Get the submitted ratings of a given movie 30 | @app.route('/api/movie/ratings/<title>') 31 | def getMovieRatings(title): 32 | ratings = graph.run('MATCH (u: User)-[r:RATED]->(m:Movie {title: {title}}) RETURN u.id AS user, r.rating AS rating', title=title) 33 | 34 | return jsonify(ratings.data()) 35 | 36 | # Get the submitted tags of a given movie 37 | @app.route('/api/movie/tags/<title>') 38 | def getMovieTags(title): 39 | tags = graph.run('MATCH (u: User)-[t:TAGGED]->(m:Movie {title: {title}}) RETURN u.id AS user, t.tag AS tag', title=title) 40 | 41 | return jsonify(tags.data()) 42 | 43 | 44 | # Get list of movies from a given year 45 | @app.route('/api/movie/year/<year>') 46 | def getMoviesByYear(year): 47 | movies = graph.run('MATCH (m:Movie) where m.year = {year} RETURN m.title AS title, m.year AS year', year=year) 48 | 49 | return jsonify(movies.data()) 50 | 51 | # Get the average rating for a given movie 52 | @app.route('/api/movie/average-rating/<title>') 53 | def getMovieAverageRating(title): 54 | avg = graph.run('MATCH (u: User)-[r:RATED]->(m:Movie {title: {title}}) RETURN m.title AS title, avg(toFloat(r.rating)) AS averageRating', title=title) 55 | 56 | return jsonify(avg.data()) 57 | 58 | ####### Top ####### 59 | 60 | # Get top N highest rated movies 61 | @app.route('/api/top/movie/top-n/<n>') 62 | def getMovieTopN(n): 63 | mvs = graph.run('MATCH (u: User )-[r:RATED]->(m:Movie) RETURN m.title AS title, avg(r.rating) AS averageRating order by averageRating desc limit toInt({n})', n=n) 64 | 65 | return mvs.data() 66 | 67 | # Get top N most rated movies 68 | @app.route('/api/top/movie/n-most-rated/<n>') 69 | def getMovieNMostRated(n): 70 | mvs = graph.run('MATCH (u: User )-[r:RATED]->(m:Movie) RETURN m.title AS title, count(r.rating) as NumberOfRatings order by NumberOfRatings desc limit toInt({n})', n=n) 71 | 72 | return mvs.data() 73 | 74 | ####### User ####### 75 | 76 | # Get the submitted ratings by a given user 77 | @app.route('/api/user/ratings/<userId>') 78 | def getUserRatings(userId): 79 | ratings = graph.run('MATCH (u:User {id: {userId}})-[r:RATED ]->(movies) RETURN movies.title AS movie, r.rating AS rating', userId=userId) 80 | 81 | return jsonify(ratings.data()) 82 | 83 | # Get the submitted tags by a given user 84 | @app.route('/api/user/tags/<userId>') 85 | def getUserTags(userId): 86 | tags = graph.run('MATCH (u:User {id: {userId}})-[t:TAGGED ]->(movies) RETURN movies.title AS title, t.tag AS tag', userId=userId) 87 | 88 | return jsonify(tags.data()) 89 | 90 | # Get the average rating by a given user 91 | @app.route('/api/user/average-rating/<userId>') 92 | def getUserAverageRating(userId): 93 | avg = graph.run('MATCH (u: User {id: {userId}})-[r:RATED]->(m:Movie) RETURN u.id AS user, avg(toFloat(r.rating)) AS averageRating', userId=userId) 94 | 95 | return jsonify(avg.data()) 96 | 97 | ##### Recommender Enginer 98 | 99 | # Content based 100 | @app.route('/api/rec_engine/content/<title>/<n>') 101 | 102 | def getRecContent(title,n): 103 | avg = graph.run('MATCH (m:Movie)<-[:IS_GENRE_OF]-(g:Genre)-[:IS_GENRE_OF]->(rec:Movie) ' 104 | 'WHERE m.title = {title} ' 105 | 'WITH rec, COLLECT(g.name) AS genres, COUNT(*) AS numberOfSharedGenres ' 106 | 'RETURN rec.title as title, genres, numberOfSharedGenres ' 107 | 'ORDER BY numberOfSharedGenres DESC LIMIT toInt({n});', title=title, n=n) 108 | 109 | return jsonify(avg.data()) 110 | 111 | # Collaborative Filtering 112 | @app.route('/api/rec_engine/collab/<userid>/<n>') 113 | 114 | def getRecCollab(userid,n): 115 | rec = graph.run('MATCH (u1:User {id:{userid}})-[r:RATED]->(m:Movie) ' 116 | 'WITH u1, avg(r.rating) AS u1_mean ' 117 | 'MATCH (u1)-[r1:RATED]->(m:Movie)<-[r2:RATED]-(u2) ' 118 | 'WITH u1, u1_mean, u2, COLLECT({r1: r1, r2: r2}) AS ratings WHERE size(ratings) > 10 ' 119 | 'MATCH (u2)-[r:RATED]->(m:Movie) ' 120 | 'WITH u1, u1_mean, u2, avg(r.rating) AS u2_mean, ratings ' 121 | 'UNWIND ratings AS r ' 122 | 'WITH sum( (r.r1.rating-u1_mean) * (r.r2.rating-u2_mean) ) AS nom, ' 123 | 'sqrt( sum( (r.r1.rating - u1_mean)^2) * sum( (r.r2.rating - u2_mean) ^2)) AS denom, u1, u2 WHERE denom <> 0 ' 124 | 'WITH u1, u2, nom/denom AS pearson ' 125 | 'ORDER BY pearson DESC LIMIT 10 ' 126 | 'MATCH (u2)-[r:RATED]->(m:Movie) WHERE NOT EXISTS( (u1)-[:RATED]->(m) ) ' 127 | 'RETURN m.title AS title, SUM( pearson * r.rating) AS score ' 128 | 'ORDER BY score DESC LIMIT toInt({n});', userid=userid, n=n) 129 | 130 | return jsonify(rec.data()) 131 | 132 | if __name__ == '__main__': 133 | 134 | # Create the application instance 135 | app = connexion.App(__name__, specification_dir='swagger/') 136 | 137 | # Read the swagger.yml file to configure the endpoints 138 | app.add_api('swagger.yml') 139 | 140 | app.run(port=5000, host='0.0.0.0', debug=True) -------------------------------------------------------------------------------- /docker/ingestion/ingestion.py: -------------------------------------------------------------------------------- 1 | import csv 2 | from py2neo import Graph, Node 3 | import os 4 | import time 5 | 6 | # wait for Neo4j in Docker 7 | time.sleep(15) 8 | 9 | N_MOVIES = 1000 10 | N_RATINGS = 1000 11 | N_TAGS = 1000 12 | N_LINKS = 1000 13 | 14 | # NEO4J_HOST will be provided by Docker, otherwise localhost 15 | 16 | HOST = os.environ.get("NEO4J_HOST", "localhost") 17 | PORT = 7687 18 | USER = "neo4j" 19 | PASS = "neo4j" #default 20 | 21 | graph = Graph("bolt://" + HOST + ":7687", auth=(USER, PASS)) 22 | 23 | def main(): 24 | 25 | createGenreNodes() 26 | 27 | print("Step 1 out of 4: loading movie nodes") 28 | loadMovies() 29 | 30 | print("Step 2 out of 4: loading rating relationships") 31 | loadRatings() 32 | 33 | print("Step 3 out of 4: loading tag relationships") 34 | loadTags() 35 | 36 | print("Step 4 out of 4: updating links to movie nodes") 37 | loadLinks() 38 | 39 | def createGenreNodes(): 40 | allGenres = ["Action", "Adventure", "Animation", "Children's", "Comedy", "Crime", 41 | "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", 42 | "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"] 43 | 44 | for genre in allGenres: 45 | gen = Node("Genre", name=genre) 46 | graph.create(gen) 47 | 48 | 49 | def loadMovies(): 50 | with open('data/movies.csv') as csvfile: 51 | readCSV = csv.reader(csvfile, delimiter=',') 52 | next(readCSV, None) # skip header 53 | for i, row in enumerate(readCSV): 54 | 55 | createMovieNodes(row) 56 | createGenreMovieRelationships(row) 57 | 58 | if (i % 100 == 0): 59 | print(f"{i}/{N_MOVIES} Movie nodes created") 60 | 61 | # break after N_MOVIES movies 62 | 63 | if i >= N_MOVIES: 64 | break 65 | 66 | def createMovieNodes(row): 67 | movieData = parseRowMovie(row) 68 | id = movieData[0] 69 | title = movieData[1] 70 | year = movieData[2] 71 | mov = Node("Movie", id=id, title=title, year=year) 72 | graph.create(mov) 73 | 74 | def parseRowMovie(row): 75 | id = row[0] 76 | year = row[1][-5:-1] 77 | title = row[1][:-7] 78 | 79 | return (id, title, year) 80 | 81 | 82 | def createGenreMovieRelationships(row): 83 | movieId = row[0] 84 | movieGenres = row[2].split("|") 85 | 86 | for movieGenre in movieGenres: 87 | graph.run('MATCH (g:Genre {name: {genre}}), (m:Movie {id: {movieId}}) CREATE (g)-[:IS_GENRE_OF]->(m)', 88 | genre=movieGenre, movieId=movieId) 89 | 90 | def parseRowGenreMovieRelationships(row): 91 | movieId = row[0] 92 | movieGenres = row[2].split("|") 93 | 94 | return (movieId, movieGenres) 95 | 96 | def loadRatings(): 97 | with open('data/ratings.csv') as csvfile: 98 | readCSV = csv.reader(csvfile, delimiter=',') 99 | next(readCSV, None) #skip header 100 | for i,row in enumerate(readCSV): 101 | createUserNodes(row) 102 | createRatingRelationship(row) 103 | 104 | if (i % 100 == 0): 105 | print(f"{i}/{N_RATINGS} Rating relationships created") 106 | 107 | if (i >= N_RATINGS): 108 | break 109 | 110 | def createUserNodes(row): 111 | user = Node("User", id="User " + row[0]) 112 | graph.merge(user, "User", "id") 113 | 114 | def createRatingRelationship(row): 115 | ratingData = parseRowRatingRelationships(row) 116 | 117 | graph.run( 118 | 'MATCH (u:User {id: {userId}}), (m:Movie {id: {movieId}}) CREATE (u)-[:RATED { rating: {rating}, timestamp: {timestamp} }]->(m)', 119 | userId=ratingData[0], movieId=ratingData[1], rating=ratingData[2], timestamp=ratingData[3]) 120 | 121 | def parseRowRatingRelationships(row): 122 | userId = "User " + row[0] 123 | movieId = row[1] 124 | rating = float(row[2]) 125 | timestamp = row[3] 126 | 127 | return (userId, movieId, rating, timestamp) 128 | 129 | def loadTags(): 130 | with open('data/tags.csv') as csvfile: 131 | readCSV = csv.reader(csvfile, delimiter=',') 132 | next(readCSV, None) #skip header 133 | for i,row in enumerate(readCSV): 134 | createTagRelationship(row) 135 | 136 | if (i % 100 == 0): 137 | print(f"{i}/{N_TAGS} Tag relationships created") 138 | 139 | if (i >= N_TAGS): 140 | break 141 | 142 | def createTagRelationship(row): 143 | tagData = parseRowTagRelationships(row) 144 | 145 | graph.run( 146 | 'MATCH (u:User {id: {userId}}), (m:Movie {id: {movieId}}) CREATE (u)-[:TAGGED { tag: {tag}, timestamp: {timestamp} }]->(m)', 147 | userId=tagData[0], movieId=tagData[1], tag=tagData[2], timestamp=tagData[3]) 148 | 149 | def parseRowTagRelationships(row): 150 | userId = "User " + row[0] 151 | movieId = row[1] 152 | tag = row[2] 153 | timestamp = row[3] 154 | 155 | return (userId, movieId, tag, timestamp) 156 | 157 | def loadLinks(): 158 | with open('data/links.csv') as csvfile: 159 | readCSV = csv.reader(csvfile, delimiter=',') 160 | next(readCSV, None) # skip header 161 | for i, row in enumerate(readCSV): 162 | 163 | updateMovieNodeWithLinks(row) 164 | 165 | if (i % 100 == 0): 166 | print(f"{i}/{N_LINKS} Movie nodes updated with links") 167 | 168 | # break after N_LINKS movies 169 | 170 | if i >= N_LINKS: 171 | break 172 | 173 | def updateMovieNodeWithLinks(row): 174 | linkData = parseRowLinks(row) 175 | 176 | graph.run( 177 | 'MATCH (m:Movie {id: {movieId}}) SET m += { imdbId: {imdbId} , tmdbId: {tmdbId} }', 178 | movieId=linkData[0], imdbId=linkData[1], tmdbId=linkData[2]) 179 | 180 | def parseRowLinks(row): 181 | movieId = row[0] 182 | imdbId = row[1] 183 | tmdbId = row[2] 184 | 185 | return (movieId, imdbId, tmdbId) 186 | 187 | 188 | if __name__ == '__main__': 189 | main() -------------------------------------------------------------------------------- /docker/api/movielens-app.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, jsonify, request 2 | from py2neo import Graph, Node, NodeMatcher, Database 3 | import connexion 4 | import os 5 | import time 6 | 7 | app = Flask(__name__) 8 | 9 | # wait for Neo4j in Docker 10 | time.sleep(15) 11 | 12 | # NEO4J_HOST will be provided by Docker, otherwise localhost 13 | 14 | HOST = os.environ.get("NEO4J_HOST", "localhost") 15 | PORT = 7687 16 | USER = "neo4j" 17 | PASS = "neo4j" #default 18 | 19 | graph = Graph("bolt://" + HOST + ":7687", auth=(USER, PASS)) 20 | 21 | ####### Movie ####### 22 | 23 | # Get the available deatils of a given movie 24 | @app.route('/api/movie/details/<title>') 25 | def getMovieData(title): 26 | matcher = NodeMatcher(graph) 27 | movie = matcher.match("Movie", title={title}).first() 28 | 29 | return jsonify(movie) 30 | 31 | # Get the genres associated with a given movie 32 | @app.route('/api/movie/genres/<title>') 33 | def getMovieGenres(title): 34 | genres = graph.run('MATCH (genres)-[:IS_GENRE_OF]->(m:Movie {title: {title}}) RETURN genres', title=title) 35 | 36 | return jsonify(list(genres)) 37 | 38 | # Get the submitted ratings of a given movie 39 | @app.route('/api/movie/ratings/<title>') 40 | def getMovieRatings(title): 41 | ratings = graph.run('MATCH (u: User)-[r:RATED]->(m:Movie {title: {title}}) RETURN u.id AS user, r.rating AS rating', title=title) 42 | 43 | return jsonify(ratings.data()) 44 | 45 | # Get the submitted tags of a given movie 46 | @app.route('/api/movie/tags/<title>') 47 | def getMovieTags(title): 48 | tags = graph.run('MATCH (u: User)-[t:TAGGED]->(m:Movie {title: {title}}) RETURN u.id AS user, t.tag AS tag', title=title) 49 | 50 | return jsonify(tags.data()) 51 | 52 | 53 | # Get list of movies from a given year 54 | @app.route('/api/movie/year/<year>') 55 | def getMoviesByYear(year): 56 | movies = graph.run('MATCH (m:Movie) where m.year = {year} RETURN m.title AS title, m.year AS year', year=year) 57 | 58 | return jsonify(movies.data()) 59 | 60 | # Get the average rating for a given movie 61 | @app.route('/api/movie/average-rating/<title>') 62 | def getMovieAverageRating(title): 63 | avg = graph.run('MATCH (u: User)-[r:RATED]->(m:Movie {title: {title}}) RETURN m.title AS title, avg(toFloat(r.rating)) AS averageRating', title=title) 64 | 65 | return jsonify(avg.data()) 66 | 67 | ####### Top ####### 68 | 69 | # Get top N highest rated movies 70 | @app.route('/api/top/movie/top-n/<n>') 71 | def getMovieTopN(n): 72 | mvs = graph.run('MATCH (u: User )-[r:RATED]->(m:Movie) RETURN m.title AS title, avg(r.rating) AS averageRating order by averageRating desc limit toInt({n})', n=n) 73 | 74 | return mvs.data() 75 | 76 | # Get top N most rated movies 77 | @app.route('/api/top/movie/n-most-rated/<n>') 78 | def getMovieNMostRated(n): 79 | mvs = graph.run('MATCH (u: User )-[r:RATED]->(m:Movie) RETURN m.title AS title, count(r.rating) as NumberOfRatings order by NumberOfRatings desc limit toInt({n})', n=n) 80 | 81 | return mvs.data() 82 | 83 | ####### User ####### 84 | 85 | # Get the submitted ratings by a given user 86 | @app.route('/api/user/ratings/<userId>') 87 | def getUserRatings(userId): 88 | ratings = graph.run('MATCH (u:User {id: {userId}})-[r:RATED ]->(movies) RETURN movies.title AS movie, r.rating AS rating', userId=userId) 89 | 90 | return jsonify(ratings.data()) 91 | 92 | # Get the submitted tags by a given user 93 | @app.route('/api/user/tags/<userId>') 94 | def getUserTags(userId): 95 | tags = graph.run('MATCH (u:User {id: {userId}})-[t:TAGGED ]->(movies) RETURN movies.title AS title, t.tag AS tag', userId=userId) 96 | 97 | return jsonify(tags.data()) 98 | 99 | # Get the average rating by a given user 100 | @app.route('/api/user/average-rating/<userId>') 101 | def getUserAverageRating(userId): 102 | avg = graph.run('MATCH (u: User {id: {userId}})-[r:RATED]->(m:Movie) RETURN u.id AS user, avg(toFloat(r.rating)) AS averageRating', userId=userId) 103 | 104 | return jsonify(avg.data()) 105 | 106 | ####### Recommender Engine ####### 107 | 108 | # Content based 109 | @app.route('/api/rec_engine/content/<title>/<n>') 110 | 111 | def getRecContent(title,n): 112 | rec = graph.run('MATCH (m:Movie)<-[:IS_GENRE_OF]-(g:Genre)-[:IS_GENRE_OF]->(rec:Movie) ' 113 | 'WHERE m.title = {title} ' 114 | 'WITH rec, COLLECT(g.name) AS genres, COUNT(*) AS numberOfSharedGenres ' 115 | 'RETURN rec.title as title, genres, numberOfSharedGenres ' 116 | 'ORDER BY numberOfSharedGenres DESC LIMIT toInt({n});', title=title, n=n) 117 | 118 | return jsonify(rec.data()) 119 | 120 | # Collaborative Filtering 121 | @app.route('/api/rec_engine/collab/<userid>/<n>') 122 | 123 | def getRecCollab(userid,n): 124 | rec = graph.run('MATCH (u1:User {id:{userid}})-[r:RATED]->(m:Movie) ' 125 | 'WITH u1, avg(r.rating) AS u1_mean ' 126 | 'MATCH (u1)-[r1:RATED]->(m:Movie)<-[r2:RATED]-(u2) ' 127 | 'WITH u1, u1_mean, u2, COLLECT({r1: r1, r2: r2}) AS ratings WHERE size(ratings) > 10 ' 128 | 'MATCH (u2)-[r:RATED]->(m:Movie) ' 129 | 'WITH u1, u1_mean, u2, avg(r.rating) AS u2_mean, ratings ' 130 | 'UNWIND ratings AS r ' 131 | 'WITH sum( (r.r1.rating-u1_mean) * (r.r2.rating-u2_mean) ) AS nom, ' 132 | 'sqrt( sum( (r.r1.rating - u1_mean)^2) * sum( (r.r2.rating - u2_mean) ^2)) AS denom, u1, u2 WHERE denom <> 0 ' 133 | 'WITH u1, u2, nom/denom AS pearson ' 134 | 'ORDER BY pearson DESC LIMIT 10 ' 135 | 'MATCH (u2)-[r:RATED]->(m:Movie) WHERE NOT EXISTS( (u1)-[:RATED]->(m) ) ' 136 | 'RETURN m.title AS title, SUM( pearson * r.rating) AS score ' 137 | 'ORDER BY score DESC LIMIT toInt({n});', userid=userid, n=n) 138 | 139 | return jsonify(rec.data()) 140 | 141 | if __name__ == '__main__': 142 | 143 | # Create the application instance 144 | app = connexion.App(__name__, specification_dir='swagger/') 145 | 146 | # Read the swagger.yml file to configure the endpoints 147 | app.add_api('swagger.yml') 148 | 149 | app.run(port=5000, host='0.0.0.0', debug=True) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MovieLens in Neo4j 2 | 3 | Load MovieLens dataset into Neo4j and provide an API to retrieve data. 4 | 5 | ![](https://i.imgur.com/uHgJsHp.png) 6 | 7 | ## Table of contents 8 | 9 | * [Requirements](#requirements) 10 | * [Project structure](#project-structure) 11 | * [Data](#data) 12 | * [Graph structure](#graph-structure) 13 | * [Ingestion](#ingestion) 14 | * [API](#api) 15 | * [Docker](#docker) 16 | * [Recommender Engine](#recommender-engine) 17 | 18 | 19 | ## Requirements 20 | 21 | * python 3.7 22 | * py2neo 23 | * neo4j 24 | * flask 25 | * swagger 26 | * connexion 27 | 28 | ## Project structure 29 | 30 | ``` 31 | ├── api/ 32 | │ │── swagger/ 33 | │ │ └── swagger.yml 34 | │ └── movielens-app.py│ 35 | │ 36 | ├── docker/ 37 | │ └── ... 38 | │ 39 | ├── ingestion/ 40 | │ │── data/ 41 | │ │ │── links.csv 42 | │ │ │── movies.csv 43 | │ │ │── ratings.csv 44 | │ │ └── tags.csv 45 | │ │── test/ 46 | │ │ └── ingestion_tests.py 47 | │ └── ingestion.py 48 | │ 49 | └── README.md 50 | ``` 51 | 52 | 53 | ## Data 54 | 55 | MovieLens 20M Dataset 56 | 57 | 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users. 58 | 59 | 60 | ##### Ratings Data File Structure (ratings.csv) 61 | 62 | All ratings are contained in the file `ratings.csv`. Each line of this file after the header row represents one rating of one movie by one user, and has the following format: 63 | 64 | userId,movieId,rating,timestamp 65 | 66 | ##### Tags Data File Structure (tags.csv) 67 | 68 | All tags are contained in the file `tags.csv`. Each line of this file after the header row represents one tag applied to one movie by one user, and has the following format: 69 | 70 | userId,movieId,tag,timestamp 71 | 72 | ##### Movies Data File Structure (movies.csv) 73 | 74 | Movie information is contained in the file `movies.csv`. Each line of this file after the header row represents one movie, and has the following format: 75 | 76 | movieId,title,genres 77 | 78 | ##### Links Data File Structure (links.csv) 79 | 80 | Identifiers that can be used to link to other sources of movie data are contained in the file `links.csv`. Each line of this file after the header row represents one movie, and has the following format: 81 | 82 | movieId,imdbId,tmdbId 83 | 84 | ## Graph Structure 85 | 86 | The graph structure consists of nodes with 3 distinct *labels* (**Genre**, **Movie**, **User**), and 3 *relationships* (**RATED**, **TAGGED**, **IS_GENRE_OF**). Links are added as additional *properties* to movie nodes. 87 | 88 | ![](https://i.imgur.com/PW1GohY.png "Logo Title Text 1") 89 | 90 | ## Ingestion 91 | 92 | Python script (*ingestion.py*) that loads MovieLens dataset into Neo4j in a graph structure. 93 | 94 | ### Steps 95 | 96 | * Create **Genre** nodes 97 | * Load *movies.csv* 98 | * Create **Movie** nodes 99 | * Create Movie-Genre relationships 100 | * Load *ratings.csv* 101 | * Create **User** nodes 102 | * Create User-Movie **rating** relationships 103 | * Load *tags.csv* 104 | * Create User-Movie **tag** relationships 105 | * Load *links.csv* 106 | * Update Movie nodes properties with links 107 | 108 | ## API 109 | 110 | ### Description 111 | 112 | API documentation is generated using *Swagger* and *Connexion*. 113 | 114 | One example: 115 | 116 | **/api/movie/ratings/[TITLE]** 117 | 118 | Returns the ratings submitted for a given movie. 119 | 120 | http://localhost:5000/api/movie/ratings/Braveheart 121 | 122 | will return: 123 | 124 | ``` 125 | [ 126 | { 127 | "rating": 4.0, 128 | "user": "User 1" 129 | }, 130 | { 131 | "rating": 4.0, 132 | "user": "User 5" 133 | }, 134 | { 135 | "rating": 5.0, 136 | "user": "User 6" 137 | } 138 | ] 139 | ``` 140 | 141 | ### Documentation 142 | 143 | When docker compose up is finished go to http://localhost:5000/api/ui to see the full documentation. 144 | 145 | ![](https://i.imgur.com/dNv44FA.png) 146 | 147 | For example: 148 | 149 | ![](https://i.imgur.com/aLFprys.png) 150 | 151 | 152 | ## Docker 153 | 154 | ### Instructions 155 | 156 | * *cd* into folder 157 | * run ``` docker-compose up ``` 158 | * wait for ingestion to finish 159 | 160 | <img src="https://i.imgur.com/AoPs8hE.png" width="400"> 161 | 162 | * open Neo4j UI at http://localhost:7474 163 | * open API documentation at http://localhost:5000/api/ui 164 | 165 | 166 | 167 | **For the Docker solution the MovieLens version with 100K ratings was used.** 168 | 169 | If you want to use the 20M dataset: 170 | 171 | * download dataset from http://files.grouplens.org/datasets/movielens/ml-20m.zip 172 | * move unzipped data into *docker/ingestion/data* 173 | * then follow instructions above 174 | 175 | **By default it only loads 1000 movies/links/ratings/tags.** 176 | 177 | If you want to increase that, you can do so by changing *ingestion.py*: 178 | 179 | ```python 180 | N_MOVIES = 1000 181 | N_RATINGS = 1000 182 | N_TAGS = 1000 183 | N_LINKS = 1000 184 | ``` 185 | 186 | If only a subset is used, some relationships might not be created due to missing nodes. 187 | 188 | ### Structure 189 | 190 | ``` 191 | docker/ 192 | ├── api/ 193 | │ │── swagger/ 194 | │ │ └── swagger.yml 195 | │ │── Dockerfile 196 | │ │── movielens-app.py 197 | │ └── requirements.txt 198 | │ 199 | ├── ingestion/ 200 | │ │── data/ 201 | │ │ │── links.csv 202 | │ │ │── movies.csv 203 | │ │ │── ratings.csv 204 | │ │ └── tags.csv 205 | │ │── Dockerfile 206 | │ │── ingestion.py 207 | │ └── requirements.tx 208 | │ 209 | └── docker-compose.yml 210 | ``` 211 | 212 | 213 | ## Recommender Engine 214 | 215 | Based on: http://guides.neo4j.com/sandbox/recommendations 216 | 217 | ### Content-based 218 | 219 | Recommend top *N* movies for a given movie, based on common genres. 220 | 221 | ```/api/rec_engine/content/[TITLE]/[N]``` 222 | 223 | **Example:** 224 | 225 | Top 3 movies similar to *Braveheart*. 226 | 227 | http://localhost:5000/api/rec_engine/content/Braveheart/3 228 | 229 | Returns: 230 | 231 | ``` 232 | [ 233 | { 234 | "genres": [ 235 | "Action", 236 | "Drama", 237 | "War" 238 | ], 239 | "numberOfSharedGenres": 3, 240 | "title": "Courage Under Fire" 241 | }, 242 | { 243 | "genres": [ 244 | "Action", 245 | "Drama", 246 | "War" 247 | ], 248 | "numberOfSharedGenres": 3, 249 | "title": "Great Escape, The" 250 | }, 251 | { 252 | "genres": [ 253 | "Action", 254 | "Drama", 255 | "War" 256 | ], 257 | "numberOfSharedGenres": 3, 258 | "title": "Henry V" 259 | } 260 | ] 261 | ``` 262 | 263 | Cypher query in Neo4j: 264 | 265 | ```cypher 266 | MATCH (m:Movie)<-[:IS_GENRE_OF]-(g:Genre)-[:IS_GENRE_OF]->(rec:Movie) 267 | WHERE m.title = [TITLE] 268 | WITH rec, COLLECT(g.name) AS genres, COUNT(*) AS sharedGenres 269 | RETURN rec.title as title, genres, sharedGenres 270 | ORDER BY sharedGenres DESC LIMIT [N]; 271 | ``` 272 | 273 | ### Collaborative Filtering 274 | 275 | Recommend top *N* movies for a given user, based on collaborative filtering. For this to work properly much more than 1000 ratings should be loaded. 276 | 277 | ```/api/rec_engine/collab/[USER_ID]/[N]``` 278 | 279 | **Example:** 280 | 281 | For *User 1* return top *5* movies. 282 | 283 | http://localhost:5000/api/rec_engine/collab/User%201/5 284 | 285 | Returns: 286 | 287 | ``` 288 | [ 289 | { 290 | "score": 2.7610991022551645, 291 | "title": "Eat Drink Man Woman (Yin shi nan nu)" 292 | }, 293 | { 294 | "score": 2.1133083447185044, 295 | "title": "Heavenly Creatures" 296 | }, 297 | { 298 | "score": 2.0585623586160793, 299 | "title": "Living in Oblivion" 300 | }, 301 | { 302 | "score": 2.0585623586160793, 303 | "title": "Notorious" 304 | }, 305 | { 306 | "score": 2.0585623586160793, 307 | "title": "High Noon" 308 | } 309 | ] 310 | ``` 311 | Cypher query in Neo4j: 312 | 313 | ``` 314 | MATCH (u1:User {id:[USER_ID]})-[r:RATED]->(m:Movie) 315 | WITH u1, avg(r.rating) AS u1_mean 316 | 317 | MATCH (u1)-[r1:RATED]->(m:Movie)<-[r2:RATED]-(u2) 318 | WITH u1, u1_mean, u2, COLLECT({r1: r1, r2: r2}) AS ratings WHERE size(ratings) > 10 319 | 320 | MATCH (u2)-[r:RATED]->(m:Movie) 321 | WITH u1, u1_mean, u2, avg(r.rating) AS u2_mean, ratings 322 | 323 | UNWIND ratings AS r 324 | 325 | WITH sum( (r.r1.rating-u1_mean) * (r.r2.rating-u2_mean) ) AS nom, 326 | sqrt( sum( (r.r1.rating - u1_mean)^2) * sum( (r.r2.rating - u2_mean) ^2)) AS denom, 327 | u1, u2 WHERE denom <> 0 328 | 329 | WITH u1, u2, nom/denom AS pearson 330 | ORDER BY pearson DESC LIMIT 10 331 | 332 | MATCH (u2)-[r:RATED]->(m:Movie) WHERE NOT EXISTS( (u1)-[:RATED]->(m) ) 333 | 334 | RETURN m.title, SUM( pearson * r.rating) AS score 335 | ORDER BY score DESC LIMIT [N] 336 | ``` -------------------------------------------------------------------------------- /api/swagger/swagger.yml: -------------------------------------------------------------------------------- 1 | swagger: "2.0" 2 | info: 3 | description: This is the API documentation on how to interact with MovieLens data in Neo4j. 4 | version: "1.0.0" 5 | title: MovieLens - Neo4j API Documentation 6 | consumes: 7 | - "application/json" 8 | produces: 9 | - "application/json" 10 | 11 | basePath: "/api" 12 | 13 | # Paths supported by the server application 14 | paths: 15 | /movie/details/{title}: 16 | get: 17 | operationId: "movielens-app.getMovieData" 18 | summary: "Get the available details about a given movie." 19 | description: "Get the available details about a given movie." 20 | "tags": [ 21 | "Movie" 22 | ] 23 | parameters: 24 | - name: title 25 | in: path 26 | description: movie title 27 | required: true 28 | type: string 29 | responses: 30 | 200: 31 | description: "Returns movie details." 32 | schema: 33 | type: "array" 34 | items: 35 | properties: 36 | id: 37 | type: "string" 38 | imdbId: 39 | type: "string" 40 | title: 41 | type: "string" 42 | tmdbId: 43 | type: "string" 44 | year: 45 | type: "string" 46 | 47 | /movie/genres/{title}: 48 | get: 49 | operationId: "movielens-app.getMovieGenres" 50 | summary: "Get the genres associated with a given movie." 51 | description: "Get the genres associated with given movie." 52 | "tags": [ 53 | "Movie" 54 | ] 55 | parameters: 56 | - name: title 57 | in: path 58 | description: movie title 59 | required: true 60 | type: string 61 | responses: 62 | 200: 63 | description: "Returns genres." 64 | schema: 65 | type: "array" 66 | items: 67 | properties: 68 | name: 69 | type: "string" 70 | 71 | /movie/ratings/{title}: 72 | get: 73 | operationId: "movielens-app.getMovieRatings" 74 | summary: "Get the ratings submitted for a given movie." 75 | description: "Get the ratings submitted for a given movie." 76 | "tags": [ 77 | "Movie" 78 | ] 79 | parameters: 80 | - name: title 81 | in: path 82 | description: movie title 83 | required: true 84 | type: string 85 | responses: 86 | 200: 87 | description: "Returns movie ratings." 88 | schema: 89 | type: "array" 90 | items: 91 | properties: 92 | rating: 93 | type: "string" 94 | user: 95 | type: "string" 96 | 97 | /movie/tags/{title}: 98 | get: 99 | operationId: "movielens-app.getMovieTags" 100 | summary: "Get the tags submitted for a given movie." 101 | description: "Get the tags submitted for a given movie." 102 | "tags": [ 103 | "Movie" 104 | ] 105 | parameters: 106 | - name: title 107 | in: path 108 | description: movie title 109 | required: true 110 | type: string 111 | responses: 112 | 200: 113 | description: "returns movie data" 114 | schema: 115 | type: "array" 116 | items: 117 | properties: 118 | tag: 119 | type: "string" 120 | user: 121 | type: "string" 122 | 123 | /movie/year/{year}: 124 | get: 125 | operationId: "movielens-app.getMoviesByYear" 126 | summary: "Get movies from a given year." 127 | description: "Get movies from a given year." 128 | "tags": [ 129 | "Movie" 130 | ] 131 | parameters: 132 | - name: year 133 | in: path 134 | description: movie title 135 | required: true 136 | type: string 137 | responses: 138 | 200: 139 | description: "Returns movies from the given year." 140 | schema: 141 | type: "array" 142 | items: 143 | properties: 144 | title: 145 | type: "string" 146 | year: 147 | type: "string" 148 | 149 | /movie/average-rating/{title}: 150 | get: 151 | operationId: "movielens-app.getMovieAverageRating" 152 | summary: "Get the average rating of movie." 153 | description: "Get the average rating of movie." 154 | "tags": [ 155 | "Movie" 156 | ] 157 | parameters: 158 | - name: title 159 | in: path 160 | description: movie title 161 | required: true 162 | type: string 163 | responses: 164 | 200: 165 | description: "Returns average rating for a given movie." 166 | schema: 167 | type: "array" 168 | items: 169 | properties: 170 | averageRating: 171 | type: "string" 172 | title: 173 | type: "string" 174 | 175 | /top/movie/top-n/{n}: 176 | get: 177 | operationId: "movielens-app.getMovieTopN" 178 | summary: "Get top N highest rated movies." 179 | description: "Get top N highest rated movies.." 180 | "tags": [ 181 | "Top" 182 | ] 183 | parameters: 184 | - name: n 185 | in: path 186 | description: movie title 187 | required: true 188 | type: string 189 | responses: 190 | 200: 191 | description: "Returns top N highest rated movies." 192 | schema: 193 | type: "array" 194 | items: 195 | properties: 196 | averageRating: 197 | type: "string" 198 | title: 199 | type: "string" 200 | 201 | /top/movie/n-most-rated/{n}: 202 | get: 203 | operationId: "movielens-app.getMovieNMostRated" 204 | summary: "Get N most rated movies." 205 | description: "Get N most rated movies." 206 | "tags": [ 207 | "Top" 208 | ] 209 | parameters: 210 | - name: n 211 | in: path 212 | description: movie title 213 | required: true 214 | type: string 215 | responses: 216 | 200: 217 | description: "Returns N most rated movies." 218 | schema: 219 | type: "array" 220 | items: 221 | properties: 222 | NumberOfRatings: 223 | type: "string" 224 | title: 225 | type: "string" 226 | 227 | /user/ratings/{userId}: 228 | get: 229 | operationId: "movielens-app.getUserRatings" 230 | summary: "Get the ratings submitted by a given user." 231 | description: "Get the ratings submitted by a given user." 232 | "tags": [ 233 | "User" 234 | ] 235 | parameters: 236 | - name: userId 237 | in: path 238 | description: movie title 239 | required: true 240 | type: string 241 | responses: 242 | 200: 243 | description: "Get the ratings submitted by a given user." 244 | schema: 245 | type: "array" 246 | items: 247 | properties: 248 | movie: 249 | type: "string" 250 | rating: 251 | type: "string" 252 | 253 | /user/tags/{userId}: 254 | get: 255 | operationId: "movielens-app.getUserTags" 256 | summary: "Get the tags submitted by a given user." 257 | description: "Get the tags submitted by a given user." 258 | "tags": [ 259 | "User" 260 | ] 261 | parameters: 262 | - name: userId 263 | in: path 264 | description: movie title 265 | required: true 266 | type: string 267 | responses: 268 | 200: 269 | description: "Returns the tags submitted by a given user." 270 | schema: 271 | type: "array" 272 | items: 273 | properties: 274 | tag: 275 | type: "string" 276 | title: 277 | type: "string" 278 | 279 | /user/average-rating/{userId}: 280 | get: 281 | operationId: "movielens-app.getUserAverageRating" 282 | summary: "Get the average ratings submitted by a given user." 283 | description: "Get the average ratings submitted by a given user." 284 | "tags": [ 285 | "User" 286 | ] 287 | parameters: 288 | - name: userId 289 | in: path 290 | description: movie title 291 | required: true 292 | type: string 293 | responses: 294 | 200: 295 | description: "Returns the average ratings submitted by a given user." 296 | schema: 297 | type: "array" 298 | items: 299 | properties: 300 | averageRating: 301 | type: "string" 302 | user: 303 | type: "string" 304 | 305 | /rec_engine/content/{title}/{n}: 306 | get: 307 | operationId: "movielens-app.getRecContent" 308 | summary: "Content-based recommender based on common genres." 309 | description: "Content-based recommender based on common genres." 310 | "tags": [ 311 | "Recommender Engine" 312 | ] 313 | parameters: 314 | - name: title 315 | in: path 316 | description: movie title 317 | required: true 318 | type: string 319 | - name: n 320 | in: path 321 | description: n of recommendations 322 | required: true 323 | type: string 324 | responses: 325 | 200: 326 | description: "Returns top N recommendations based on genres." 327 | schema: 328 | type: "array" 329 | items: 330 | properties: 331 | genres: 332 | type: "string" 333 | numberOfSharedGenres: 334 | type: "string" 335 | title: 336 | type: "string" 337 | 338 | /rec_engine/collab/{userid}/{n}: 339 | get: 340 | operationId: "movielens-app.getRecCollab" 341 | summary: "Recommend top N movies based on collaborative filtering." 342 | description: "Recommend top N movies based on collaborative filtering." 343 | "tags": [ 344 | "Recommender Engine" 345 | ] 346 | parameters: 347 | - name: userid 348 | in: path 349 | description: userid 350 | required: true 351 | type: string 352 | - name: n 353 | in: path 354 | description: n of recommendations 355 | required: true 356 | type: string 357 | responses: 358 | 200: 359 | description: "Returns top N recommendations based on collaborative filtering." 360 | schema: 361 | type: "array" 362 | items: 363 | properties: 364 | title: 365 | type: "string" 366 | score: 367 | type: "string" -------------------------------------------------------------------------------- /docker/api/swagger/swagger.yml: -------------------------------------------------------------------------------- 1 | swagger: "2.0" 2 | info: 3 | description: This is the API documentation on how to interact with MovieLens data in Neo4j. 4 | version: "1.0.0" 5 | title: MovieLens - Neo4j API Documentation 6 | consumes: 7 | - "application/json" 8 | produces: 9 | - "application/json" 10 | 11 | basePath: "/api" 12 | 13 | # Paths supported by the server application 14 | paths: 15 | /movie/details/{title}: 16 | get: 17 | operationId: "movielens-app.getMovieData" 18 | summary: "Get the available details about a given movie." 19 | description: "Get the available details about a given movie." 20 | "tags": [ 21 | "Movie" 22 | ] 23 | parameters: 24 | - name: title 25 | in: path 26 | description: movie title 27 | required: true 28 | type: string 29 | responses: 30 | 200: 31 | description: "Returns movie details." 32 | schema: 33 | type: "array" 34 | items: 35 | properties: 36 | id: 37 | type: "string" 38 | imdbId: 39 | type: "string" 40 | title: 41 | type: "string" 42 | tmdbId: 43 | type: "string" 44 | year: 45 | type: "string" 46 | 47 | /movie/genres/{title}: 48 | get: 49 | operationId: "movielens-app.getMovieGenres" 50 | summary: "Get the genres associated with a given movie." 51 | description: "Get the genres associated with given movie." 52 | "tags": [ 53 | "Movie" 54 | ] 55 | parameters: 56 | - name: title 57 | in: path 58 | description: movie title 59 | required: true 60 | type: string 61 | responses: 62 | 200: 63 | description: "Returns genres." 64 | schema: 65 | type: "array" 66 | items: 67 | properties: 68 | name: 69 | type: "string" 70 | 71 | /movie/ratings/{title}: 72 | get: 73 | operationId: "movielens-app.getMovieRatings" 74 | summary: "Get the ratings submitted for a given movie." 75 | description: "Get the ratings submitted for a given movie." 76 | "tags": [ 77 | "Movie" 78 | ] 79 | parameters: 80 | - name: title 81 | in: path 82 | description: movie title 83 | required: true 84 | type: string 85 | responses: 86 | 200: 87 | description: "Returns movie ratings." 88 | schema: 89 | type: "array" 90 | items: 91 | properties: 92 | rating: 93 | type: "string" 94 | user: 95 | type: "string" 96 | 97 | /movie/tags/{title}: 98 | get: 99 | operationId: "movielens-app.getMovieTags" 100 | summary: "Get the tags submitted for a given movie." 101 | description: "Get the tags submitted for a given movie." 102 | "tags": [ 103 | "Movie" 104 | ] 105 | parameters: 106 | - name: title 107 | in: path 108 | description: movie title 109 | required: true 110 | type: string 111 | responses: 112 | 200: 113 | description: "returns movie data" 114 | schema: 115 | type: "array" 116 | items: 117 | properties: 118 | tag: 119 | type: "string" 120 | user: 121 | type: "string" 122 | 123 | /movie/year/{year}: 124 | get: 125 | operationId: "movielens-app.getMoviesByYear" 126 | summary: "Get movies from a given year." 127 | description: "Get movies from a given year." 128 | "tags": [ 129 | "Movie" 130 | ] 131 | parameters: 132 | - name: year 133 | in: path 134 | description: movie title 135 | required: true 136 | type: string 137 | responses: 138 | 200: 139 | description: "Returns movies from the given year." 140 | schema: 141 | type: "array" 142 | items: 143 | properties: 144 | title: 145 | type: "string" 146 | year: 147 | type: "string" 148 | 149 | /movie/average-rating/{title}: 150 | get: 151 | operationId: "movielens-app.getMovieAverageRating" 152 | summary: "Get the average rating of movie." 153 | description: "Get the average rating of movie." 154 | "tags": [ 155 | "Movie" 156 | ] 157 | parameters: 158 | - name: title 159 | in: path 160 | description: movie title 161 | required: true 162 | type: string 163 | responses: 164 | 200: 165 | description: "Returns average rating for a given movie." 166 | schema: 167 | type: "array" 168 | items: 169 | properties: 170 | averageRating: 171 | type: "string" 172 | title: 173 | type: "string" 174 | 175 | /top/movie/top-n/{n}: 176 | get: 177 | operationId: "movielens-app.getMovieTopN" 178 | summary: "Get top N highest rated movies." 179 | description: "Get top N highest rated movies.." 180 | "tags": [ 181 | "Top" 182 | ] 183 | parameters: 184 | - name: n 185 | in: path 186 | description: movie title 187 | required: true 188 | type: string 189 | responses: 190 | 200: 191 | description: "Returns top N highest rated movies." 192 | schema: 193 | type: "array" 194 | items: 195 | properties: 196 | averageRating: 197 | type: "string" 198 | title: 199 | type: "string" 200 | 201 | /top/movie/n-most-rated/{n}: 202 | get: 203 | operationId: "movielens-app.getMovieNMostRated" 204 | summary: "Get N most rated movies." 205 | description: "Get N most rated movies." 206 | "tags": [ 207 | "Top" 208 | ] 209 | parameters: 210 | - name: n 211 | in: path 212 | description: movie title 213 | required: true 214 | type: string 215 | responses: 216 | 200: 217 | description: "Returns N most rated movies." 218 | schema: 219 | type: "array" 220 | items: 221 | properties: 222 | NumberOfRatings: 223 | type: "string" 224 | title: 225 | type: "string" 226 | 227 | /user/ratings/{userId}: 228 | get: 229 | operationId: "movielens-app.getUserRatings" 230 | summary: "Get the ratings submitted by a given user." 231 | description: "Get the ratings submitted by a given user." 232 | "tags": [ 233 | "User" 234 | ] 235 | parameters: 236 | - name: userId 237 | in: path 238 | description: movie title 239 | required: true 240 | type: string 241 | responses: 242 | 200: 243 | description: "Get the ratings submitted by a given user." 244 | schema: 245 | type: "array" 246 | items: 247 | properties: 248 | movie: 249 | type: "string" 250 | rating: 251 | type: "string" 252 | 253 | /user/tags/{userId}: 254 | get: 255 | operationId: "movielens-app.getUserTags" 256 | summary: "Get the tags submitted by a given user." 257 | description: "Get the tags submitted by a given user." 258 | "tags": [ 259 | "User" 260 | ] 261 | parameters: 262 | - name: userId 263 | in: path 264 | description: movie title 265 | required: true 266 | type: string 267 | responses: 268 | 200: 269 | description: "Returns the tags submitted by a given user." 270 | schema: 271 | type: "array" 272 | items: 273 | properties: 274 | tag: 275 | type: "string" 276 | title: 277 | type: "string" 278 | 279 | /user/average-rating/{userId}: 280 | get: 281 | operationId: "movielens-app.getUserAverageRating" 282 | summary: "Get the average ratings submitted by a given user." 283 | description: "Get the average ratings submitted by a given user." 284 | "tags": [ 285 | "User" 286 | ] 287 | parameters: 288 | - name: userId 289 | in: path 290 | description: movie title 291 | required: true 292 | type: string 293 | responses: 294 | 200: 295 | description: "Returns the average ratings submitted by a given user." 296 | schema: 297 | type: "array" 298 | items: 299 | properties: 300 | averageRating: 301 | type: "string" 302 | user: 303 | type: "string" 304 | 305 | /rec_engine/content/{title}/{n}: 306 | get: 307 | operationId: "movielens-app.getRecContent" 308 | summary: "Content-based recommender based on common genres." 309 | description: "Content-based recommender based on common genres." 310 | "tags": [ 311 | "Recommender Engine" 312 | ] 313 | parameters: 314 | - name: title 315 | in: path 316 | description: movie title 317 | required: true 318 | type: string 319 | - name: n 320 | in: path 321 | description: n of recommendations 322 | required: true 323 | type: string 324 | responses: 325 | 200: 326 | description: "Returns top N recommendations based on genres." 327 | schema: 328 | type: "array" 329 | items: 330 | properties: 331 | genres: 332 | type: "string" 333 | numberOfSharedGenres: 334 | type: "string" 335 | title: 336 | type: "string" 337 | 338 | /rec_engine/collab/{userid}/{n}: 339 | get: 340 | operationId: "movielens-app.getRecCollab" 341 | summary: "Recommend top N movies based on collaborative filtering." 342 | description: "Recommend top N movies based on collaborative filtering." 343 | "tags": [ 344 | "Recommender Engine" 345 | ] 346 | parameters: 347 | - name: userid 348 | in: path 349 | description: userid 350 | required: true 351 | type: string 352 | - name: n 353 | in: path 354 | description: n of recommendations 355 | required: true 356 | type: string 357 | responses: 358 | 200: 359 | description: "Returns top N recommendations based on collaborative filtering." 360 | schema: 361 | type: "array" 362 | items: 363 | properties: 364 | title: 365 | type: "string" 366 | score: 367 | type: "string" 368 | -------------------------------------------------------------------------------- /ingestion/data/tags.csv: -------------------------------------------------------------------------------- 1 | userId,movieId,tag,timestamp 2 | 2,60756,funny,1445714994 3 | 2,60756,Highly quotable,1445714996 4 | 2,60756,will ferrell,1445714992 5 | 2,89774,Boxing story,1445715207 6 | 2,89774,MMA,1445715200 7 | 2,89774,Tom Hardy,1445715205 8 | 2,106782,drugs,1445715054 9 | 2,106782,Leonardo DiCaprio,1445715051 10 | 2,106782,Martin Scorsese,1445715056 11 | 7,48516,way too long,1169687325 12 | 18,431,Al Pacino,1462138765 13 | 18,431,gangster,1462138749 14 | 18,431,mafia,1462138755 15 | 18,1221,Al Pacino,1461699306 16 | 18,1221,Mafia,1461699303 17 | 18,5995,holocaust,1455735472 18 | 18,5995,true story,1455735479 19 | 18,44665,twist ending,1456948283 20 | 18,52604,Anthony Hopkins,1457650696 21 | 18,52604,courtroom drama,1457650711 22 | 18,52604,twist ending,1457650682 23 | 18,88094,britpop,1457444500 24 | 18,88094,indie record label,1457444592 25 | 18,88094,music,1457444609 26 | 18,144210,dumpster diving,1455060381 27 | 18,144210,Sustainability,1455060452 28 | 21,1569,romantic comedy,1419805413 29 | 21,1569,wedding,1419805419 30 | 21,118985,painter,1419805477 31 | 21,119141,bloody,1419793962 32 | 49,109487,black hole,1493093306 33 | 49,109487,sci-fi,1493093332 34 | 49,109487,time-travel,1493093356 35 | 62,2,fantasy,1528843929 36 | 62,2,magic board game,1528843932 37 | 62,2,Robin Williams,1528843907 38 | 62,110,beautiful scenery,1528152541 39 | 62,110,epic,1528152532 40 | 62,110,historical,1528152523 41 | 62,110,inspirational,1528152527 42 | 62,110,Medieval,1528152528 43 | 62,110,mel gibson,1528152521 44 | 62,110,Oscar (Best Cinematography),1528152539 45 | 62,110,revenge,1528152531 46 | 62,110,sword fight,1528152535 47 | 62,410,black comedy,1525636607 48 | 62,410,Christina Ricci,1525636685 49 | 62,410,Christopher Lloyd,1525636622 50 | 62,410,dark comedy,1525636610 51 | 62,410,family,1525636708 52 | 62,410,gothic,1525636609 53 | 62,2023,Al Pacino,1525636728 54 | 62,2023,Andy Garcia,1525636768 55 | 62,2023,Classic,1525636752 56 | 62,2023,Francis Ford Coppola,1525636752 57 | 62,2023,mafia,1525636733 58 | 62,2124,black comedy,1525636847 59 | 62,2124,Christina Ricci,1525636867 60 | 62,2124,Christopher Lloyd,1525636859 61 | 62,2124,Family,1525636855 62 | 62,2124,gothic,1525636849 63 | 62,2124,quirky,1525636846 64 | 62,2953,family,1525636883 65 | 62,2953,funny,1525636885 66 | 62,2953,Macaulay Culkin,1525636890 67 | 62,2953,sequel,1525636887 68 | 62,3114,animation,1525636903 69 | 62,3114,Disney,1525636902 70 | 62,3114,funny,1525636913 71 | 62,3114,original,1525636917 72 | 62,3114,Pixar,1525636901 73 | 62,3114,sequel,1525636910 74 | 62,3114,Tom Hanks,1525636925 75 | 62,3578,ancient Rome,1528152504 76 | 62,3578,Epic,1528152469 77 | 62,3578,history,1528152467 78 | 62,3578,imdb top 250,1528152498 79 | 62,3578,revenge,1528152496 80 | 62,3578,Rome,1528152463 81 | 62,3578,Russell Crowe,1528152465 82 | 62,4223,Ed Harris,1528024869 83 | 62,4223,Jude Law,1528024854 84 | 62,4223,Rachel Weisz,1528024873 85 | 62,4223,sniper,1528024852 86 | 62,4223,World War II,1528024877 87 | 62,5388,Al Pacino,1530310809 88 | 62,5388,atmospheric,1530310959 89 | 62,5388,Hilary Swank,1530310952 90 | 62,5388,Robin Williams,1530310811 91 | 62,6058,sequel,1525555053 92 | 62,6058,violent,1525555051 93 | 62,6534,dogs,1525554096 94 | 62,6534,Eric Bana,1525554086 95 | 62,6534,mad scientist,1525554098 96 | 62,6534,marvel,1525554100 97 | 62,6541,captain nemo,1525554054 98 | 62,6541,comic book,1525554028 99 | 62,6541,gothic,1525554056 100 | 62,6541,Peta Wilson,1525554040 101 | 62,6541,Sean Connery,1525554026 102 | 62,6541,superhero,1525554051 103 | 62,6564,adventure,1525554439 104 | 62,6564,Angelina Jolie,1525554432 105 | 62,6564,heroine in tight suit,1525554441 106 | 62,7153,Adventure,1528152558 107 | 62,7153,ensemble cast,1528152578 108 | 62,7153,fantasy,1528152556 109 | 62,7153,fantasy world,1528152571 110 | 62,7153,great soundtrack,1528152575 111 | 62,7153,lord of the rings,1528152581 112 | 62,7153,scenic,1528152580 113 | 62,7153,stylized,1528152577 114 | 62,7153,Tolkien,1528152561 115 | 62,8641,hilarious,1526249138 116 | 62,8641,Steve Carell,1526249156 117 | 62,8641,Will Ferrell,1526249135 118 | 62,27660,animation,1525554490 119 | 62,27660,anime,1525554488 120 | 62,27660,cyberpunk,1525554486 121 | 62,27660,dark,1525554500 122 | 62,27660,Matrix,1525554493 123 | 62,27660,sci-fi,1525554491 124 | 62,27706,dark comedy,1526248598 125 | 62,27706,Jim Carrey,1526248575 126 | 62,27706,meryl streep,1526248580 127 | 62,27706,quirky,1526248553 128 | 62,27706,stylized,1526248601 129 | 62,27808,Adam Sandler,1525554900 130 | 62,27808,family,1525554919 131 | 62,27808,sweet,1525554922 132 | 62,27831,British gangster,1532723304 133 | 62,27831,confusing,1532723364 134 | 62,27831,daniel craig,1532723305 135 | 62,27831,Exquisite plotting.,1532723355 136 | 62,27831,organized crime,1532723369 137 | 62,27831,stylish,1532723356 138 | 62,27831,Tom Hardy,1532723359 139 | 62,31658,anime,1525869041 140 | 62,31658,fantasy world,1525869051 141 | 62,31658,Hayao Miyazaki,1525869046 142 | 62,31658,Studio Ghibli,1525869044 143 | 62,33162,David Thewlis,1525554605 144 | 62,33162,Eva Green,1525554550 145 | 62,33162,Liam Neeson,1525554577 146 | 62,33162,Orlando Bloom,1525554511 147 | 62,34150,Chris Evans,1525554141 148 | 62,34150,comic book,1525554003 149 | 62,34150,heroine in tight suit,1525554138 150 | 62,34150,Jessica Alba,1525554127 151 | 62,34150,sexy female scientist,1525554144 152 | 62,34150,superhero,1525553977 153 | 62,37729,gothic,1530310527 154 | 62,37729,helena bonham carter,1530310534 155 | 62,37729,Johnny Depp,1530310529 156 | 62,37729,Tim Burton,1530310526 157 | 62,37729,visually appealing,1530310541 158 | 62,38061,black comedy,1532723380 159 | 62,38061,clever,1532723403 160 | 62,38061,fast-paced dialogue,1532723406 161 | 62,38061,good dialogue,1532723428 162 | 62,38061,Robert Downey Jr.,1532723408 163 | 62,38061,witty,1532723402 164 | 62,45447,adventure,1525637064 165 | 62,45447,Audrey Tautou,1525637075 166 | 62,45447,conspiracy theory,1525637062 167 | 62,45447,Mystery,1525637081 168 | 62,45447,Paris,1525637079 169 | 62,45447,Tom Hanks,1525637067 170 | 62,45447,treasure hunt,1525637084 171 | 62,46723,Brad Pitt,1525554217 172 | 62,46723,cate blanchett,1525554227 173 | 62,46723,multiple storylines,1525554215 174 | 62,46723,social commentary,1525554246 175 | 62,46972,Ben Stiller,1525554254 176 | 62,46972,Robin Williams,1525554255 177 | 62,46976,emma thompson,1536874692 178 | 62,46976,Maggie Gyllenhaal,1536874655 179 | 62,46976,modern fantasy,1536874658 180 | 62,46976,romance,1536874651 181 | 62,46976,surreal,1536874662 182 | 62,46976,Will Ferrell,1536874653 183 | 62,49130,Marion Cotillard,1526582371 184 | 62,49530,Africa,1536874706 185 | 62,49530,corruption,1536874704 186 | 62,49530,Jennifer Connelly,1536874701 187 | 62,49530,justice,1536874709 188 | 62,49530,Leonardo DiCaprio,1536874698 189 | 62,53464,bad acting,1525554797 190 | 62,53464,bad jokes,1525554811 191 | 62,53464,bad plot,1525554822 192 | 62,53464,Chris Evans,1525554807 193 | 62,53464,Jessica Alba,1525554794 194 | 62,53464,Marvel,1525554792 195 | 62,53464,sexy female scientist,1525554813 196 | 62,58047,Rachel Weisz,1528909364 197 | 62,58047,Ryan Reynolds,1528909347 198 | 62,59501,fantasy,1525637507 199 | 62,59501,lion,1525637517 200 | 62,59501,narnia,1525637508 201 | 62,59501,Tilda Swinton,1525637603 202 | 62,60074,bad script,1525554116 203 | 62,60074,Charlize Theron,1525554121 204 | 62,60074,Will Smith,1525554107 205 | 62,60126,Anne Hathaway,1527004240 206 | 62,60126,Steve Carell,1527004238 207 | 62,60756,comedy,1528934384 208 | 62,60756,funny,1528934381 209 | 62,60756,will ferrell,1528934379 210 | 62,61024,comedy,1528934218 211 | 62,61024,James Franco,1528934214 212 | 62,61024,Seth Rogen,1528934216 213 | 62,61024,Stoner Movie,1528934219 214 | 62,63992,audience intelligence underestimated,1528934237 215 | 62,63992,boring,1528934241 216 | 62,63992,chick flick,1528934247 217 | 62,63992,overrated,1528934240 218 | 62,63992,Teen movie,1528934233 219 | 62,68848,Adrien Brody,1527274305 220 | 62,68848,con artists,1527274320 221 | 62,68848,funny,1527274322 222 | 62,68848,interesting characters,1527274324 223 | 62,68848,Mark Ruffalo,1527274316 224 | 62,68848,Rachel Weisz,1527274311 225 | 62,71535,Bill Murray,1529777198 226 | 62,71535,dark comedy,1529777189 227 | 62,71535,Emma Stone,1529777192 228 | 62,71535,funny,1529777194 229 | 62,71535,Jesse Eisenberg,1529777186 230 | 62,71535,Woody Harrelson,1529777129 231 | 62,87430,audience intelligence underestimated,1525555172 232 | 62,87430,CGI,1525555168 233 | 62,87430,cheesy,1525555170 234 | 62,87430,comic book,1525555164 235 | 62,87430,DC,1525555176 236 | 62,87430,DC Comics,1525555184 237 | 62,87430,Ryan Reynolds,1525555162 238 | 62,87430,space,1525555182 239 | 62,87430,superhero,1525555181 240 | 62,88405,comedy,1525554879 241 | 62,88405,funny,1525554868 242 | 62,88405,happy ending,1525554872 243 | 62,88405,HOT actress,1525554880 244 | 62,88405,Justin Timberlake,1525554873 245 | 62,88405,Mila Kunis,1525554861 246 | 62,88405,New York City,1525554866 247 | 62,88405,romance,1525554883 248 | 62,88405,sex,1525554864 249 | 62,96861,Istanbul,1529611262 250 | 62,96861,Liam Neeson,1529611250 251 | 62,96861,predictable,1529611266 252 | 62,96861,Turkey,1529611256 253 | 62,96861,unnecessary sequel,1529611269 254 | 62,99114,action,1526078792 255 | 62,99114,Christoph Waltz,1526078766 256 | 62,99114,funny,1526078778 257 | 62,99114,good soundtrack,1526078785 258 | 62,99114,Great performances,1526078768 259 | 62,99114,Humour,1526078787 260 | 62,99114,Leonardo DiCaprio,1526078762 261 | 62,99114,Quentin Tarantino,1526078760 262 | 62,99114,Samuel L. Jackson,1526078763 263 | 62,99114,Soundtrack,1526078781 264 | 62,99114,western,1526078773 265 | 62,103042,Amy Adams,1525555328 266 | 62,103042,superhero,1525555319 267 | 62,104863,daniel radcliffe,1528843968 268 | 62,104863,zoe kazan,1528843965 269 | 62,107348,comedy,1528935011 270 | 62,107348,Steve Carell,1528935000 271 | 62,107348,stupid but funny,1528935013 272 | 62,107348,will ferrell,1528935002 273 | 62,108190,anti-intellectual,1525554841 274 | 62,108190,bad writing,1525554835 275 | 62,108190,dystopia,1525554830 276 | 62,108190,new society,1525554832 277 | 62,108190,plot holes,1525554845 278 | 62,108190,predictable,1525554847 279 | 62,108190,scifi,1525554839 280 | 62,108190,unintelligent,1525554843 281 | 62,111743,Charlize Theron,1530471420 282 | 62,111743,Liam Neeson,1530471434 283 | 62,111743,Seth MacFarlane,1530471419 284 | 62,111743,witty,1530471417 285 | 62,114180,action,1525554460 286 | 62,114180,dystopia,1525554450 287 | 62,114180,plot holes,1525554454 288 | 62,114180,post-apocalyptic,1525554458 289 | 62,114180,survival,1525554456 290 | 62,114180,thriller,1525554471 291 | 62,114180,unexplained,1525554473 292 | 62,114180,weak plot,1525554463 293 | 62,115149,dark hero,1528152251 294 | 62,115149,gun-fu,1528152257 295 | 62,115149,Keanu Reeves,1528152244 296 | 62,115149,Revenge,1528152241 297 | 62,115149,secret society,1528152258 298 | 62,115149,stylish,1528152247 299 | 62,116897,black comedy,1528152849 300 | 62,116897,dark comedy,1528152859 301 | 62,116897,dark humor,1528152847 302 | 62,116897,ironic,1528152854 303 | 62,116897,multiple short stories,1528152862 304 | 62,116897,short stories,1528152852 305 | 62,119141,bromance,1525555146 306 | 62,119141,comedy,1525555138 307 | 62,119141,funny,1525555129 308 | 62,119141,James Franco,1525555132 309 | 62,119141,Seth Rogen,1525555127 310 | 62,122912,comic book,1526029023 311 | 62,122912,Dr. Strange,1526029001 312 | 62,122912,Great villain,1526029025 313 | 62,122912,Guardians of the Galaxy,1526029015 314 | 62,122912,Marvel,1526028997 315 | 62,122912,MCU,1526028983 316 | 62,122912,Robert Downey Jr.,1526028991 317 | 62,122912,Thanos,1526028985 318 | 62,122912,Thor,1526029010 319 | 62,122912,Visually stunning,1526028994 320 | 62,128360,characters,1526078910 321 | 62,128360,Dialogue,1526078902 322 | 62,128360,humor,1526078901 323 | 62,128360,Kurt Russell,1526078886 324 | 62,128360,Quentin Tarantino,1526078877 325 | 62,128360,Samuel L. Jackson,1526078883 326 | 62,128360,tension building,1526078880 327 | 62,128360,violent,1526078912 328 | 62,128360,Western,1526078899 329 | 62,135133,dystopia,1525637480 330 | 62,135133,Jennifer Lawrence,1525637484 331 | 62,135133,love story,1525637487 332 | 62,135133,nonsense,1525637488 333 | 62,135133,politics,1525637499 334 | 62,135133,rebellion,1525637483 335 | 62,135133,revolution,1525637481 336 | 62,135518,Ben Kingsley,1530471365 337 | 62,135518,ethics,1530471346 338 | 62,135518,immortality,1530471343 339 | 62,135518,ryan reynolds,1530471352 340 | 62,135536,Bad story,1525555090 341 | 62,135536,Bad writing,1525555084 342 | 62,135536,Batman,1525555077 343 | 62,135536,Ben Affleck,1525555106 344 | 62,135536,comic book,1525555092 345 | 62,135536,dc comics,1525555080 346 | 62,135536,good soundtrack,1525555089 347 | 62,135536,Harley Quinn,1525555079 348 | 62,135536,Harley Quinn's ass,1525555094 349 | 62,135536,Horrible directing,1525555099 350 | 62,135536,Jared Leto,1525555072 351 | 62,135536,Joker,1525555074 352 | 62,135536,lack of plot,1525555069 353 | 62,135536,Margot Robbie,1525555073 354 | 62,135536,Poor story,1525555087 355 | 62,135536,poorly paced,1525555083 356 | 62,135536,superhero,1525555103 357 | 62,135536,visually appealing,1525555109 358 | 62,135536,Will Smith,1525555076 359 | 62,136864,action,1525555242 360 | 62,136864,batman,1525555211 361 | 62,136864,ben affleck,1525555223 362 | 62,136864,dark,1525555227 363 | 62,136864,dc comics,1525555225 364 | 62,136864,Gal Gadot,1525555240 365 | 62,136864,superhero,1525555243 366 | 62,136864,superman,1525555213 367 | 62,136864,wonderwoman,1525555233 368 | 62,139385,leonardo DiCarpio,1526079025 369 | 62,139385,survival,1526079019 370 | 62,139385,tom hardy,1526079029 371 | 62,139385,visually appealing,1526079038 372 | 62,158966,building a family,1526076353 373 | 62,158966,creative,1526076341 374 | 62,158966,freedom,1526076339 375 | 62,158966,good writing,1526076350 376 | 62,158966,individualism,1526076344 377 | 62,158966,Viggo Mortensen,1526076342 378 | 62,168248,action,1528152295 379 | 62,168248,dark hero,1528152291 380 | 62,168248,gun tactics,1528152287 381 | 62,168248,hitman,1528152293 382 | 62,168248,Keanu Reeves,1528152284 383 | 62,168248,organized crime,1528152289 384 | 62,168248,secret society,1528152282 385 | 62,174053,Dystopia,1525637341 386 | 62,174053,future,1525637343 387 | 62,174053,interesting,1525637346 388 | 62,174053,jon hamm,1525637384 389 | 62,174053,thought provoking,1525637344 390 | 62,179401,Action,1528934538 391 | 62,179401,Comedy,1528934534 392 | 62,179401,Dwayne Johnson,1528934540 393 | 62,179401,funny,1528934536 394 | 62,183611,Comedy,1526244689 395 | 62,183611,funny,1526244688 396 | 62,183611,Rachel McAdams,1526244709 397 | 62,184471,adventure,1528024900 398 | 62,184471,Alicia Vikander,1528024914 399 | 62,184471,video game adaptation,1528024898 400 | 62,187593,Josh Brolin,1527274096 401 | 62,187593,Ryan Reynolds,1527274092 402 | 62,187593,sarcasm,1527274090 403 | 62,187595,Emilia Clarke,1528934560 404 | 62,187595,star wars,1528934552 405 | 63,260,classic,1443199698 406 | 63,260,space action,1443199710 407 | 76,260,action,1439165594 408 | 76,260,sci-fi,1439165588 409 | 103,260,EPIC,1431954312 410 | 103,260,great soundtrack,1431954337 411 | 103,296,good dialogue,1431954555 412 | 103,296,great soundtrack,1431954555 413 | 103,296,non-linear,1431954555 414 | 106,4896,Everything you want is here,1467566944 415 | 106,106489,adventure,1467566979 416 | 112,260,classic sci-fi,1442535682 417 | 112,260,engrossing adventure,1442535673 418 | 112,260,EPIC,1442535666 419 | 119,260,classic,1435942530 420 | 119,260,Nerd,1435942520 421 | 119,101142,animation,1436563067 422 | 119,101142,funny,1436563067 423 | 119,101142,stone age,1436563067 424 | 119,115149,action,1437763143 425 | 119,115149,killer,1437763143 426 | 119,115149,widows/widowers,1437763143 427 | 119,115617,animation,1435944890 428 | 119,115617,kids,1435944890 429 | 119,115617,robots,1435944890 430 | 119,120635,action,1438439306 431 | 119,120635,murder,1438439306 432 | 119,120635,police,1438439306 433 | 125,1726,Kevin Costner,1474483317 434 | 125,1726,Post apocalyptic,1474483320 435 | 125,2387,dark comedy,1474382225 436 | 125,2387,dark humor,1474382223 437 | 125,3052,irreverent,1474592072 438 | 125,3052,jay and silent bob,1474592004 439 | 125,3052,Kevin Smith,1474592000 440 | 125,3052,satire,1474591997 441 | 125,5088,irreverent,1474592207 442 | 125,7022,based on a book,1474381749 443 | 125,7022,bloody,1474381755 444 | 125,7022,brutal,1474381612 445 | 125,7022,controversial,1474381812 446 | 125,7022,dystopia,1474381779 447 | 125,7022,goretastic,1474381786 448 | 125,7022,satire,1474381739 449 | 125,7022,social commentary,1474381788 450 | 125,7022,survival,1474381609 451 | 125,7022,violence,1474381745 452 | 125,7254,alternate reality,1474381441 453 | 125,7254,sci-fi,1474381448 454 | 125,7254,science fiction,1474381455 455 | 125,7254,time travel,1474381439 456 | 125,8957,brutal,1474382001 457 | 125,8957,clever,1474382019 458 | 125,8957,Disturbing,1474382015 459 | 125,8957,great ending,1474381987 460 | 125,8957,mindfuck,1474381982 461 | 125,8957,surprise ending,1474381973 462 | 125,8957,suspense,1474381994 463 | 125,9010,cruel characters,1474381529 464 | 125,42632,brutality,1474381492 465 | 125,58301,dark humor,1474381561 466 | 125,60950,threesome,1474591817 467 | 125,62434,Seth Rogen,1474592476 468 | 125,62434,Sexual Humor,1474592473 469 | 125,67695,Seth Rogen,1474382123 470 | 125,100083,embarassing scenes,1474377075 471 | 125,100083,offensive,1474377060 472 | 125,100083,R language,1474377035 473 | 125,100083,sarcasm,1474377015 474 | 125,100083,satire,1474381901 475 | 125,156371,80's,1474469700 476 | 125,158872,Crude humor,1474382035 477 | 125,158872,mindfuck,1474382087 478 | 125,158872,sarcasm,1474382053 479 | 125,158872,satire,1474382047 480 | 125,158872,Vulgar,1474382100 481 | 132,3556,sofia coppola,1163148016 482 | 132,6323,John Cusack,1161800041 483 | 132,6367,Ewan McGregor,1161799885 484 | 132,6367,Renee Zellweger,1161799951 485 | 138,59103,jackie chan,1222676394 486 | 138,59103,kung fu,1222676405 487 | 161,52287,Something for everyone in this one... saw it without and plan on seeing it with kids!,1176498861 488 | 166,293,assassin,1188774484 489 | 166,293,Jean Reno,1188774487 490 | 166,54286,assassin,1188774519 491 | 166,54286,assassin-in-training (scene),1188774517 492 | 166,54286,espionage,1188774515 493 | 166,54286,Robert Ludlum,1188774524 494 | 167,104,test tag,1154718872 495 | 177,115617,feel-good,1435523876 496 | 177,115617,fun family movie,1435523876 497 | 177,115617,very funny,1435523876 498 | 184,2579,black and white,1537094307 499 | 184,2579,Christopher Nolan,1537094326 500 | 184,2579,directorial debut,1537094323 501 | 184,2579,mindfuck,1537094375 502 | 184,2579,not linear,1537094318 503 | 184,2579,Twist Ending,1537094309 504 | 184,3793,action,1537094381 505 | 184,3793,comic book,1537094359 506 | 184,3793,hugh jackman,1537094370 507 | 184,3793,marvel,1537094366 508 | 184,3793,superhero,1537094354 509 | 184,4226,dark,1537094453 510 | 184,4226,Mindfuck,1537094464 511 | 184,4226,nonlinear,1537094445 512 | 184,4226,psychology,1537094448 513 | 184,4226,twist ending,1537094447 514 | 184,4896,alan rickman,1537094576 515 | 184,4896,harry potter,1537094544 516 | 184,4896,humorous,1537094550 517 | 184,4896,Magic,1537094530 518 | 184,5388,atmospheric,1537094680 519 | 184,5388,insomnia,1537094688 520 | 184,5388,thought-provoking,1537094685 521 | 184,6283,amazing artwork,1537094506 522 | 184,6283,anime,1537094487 523 | 184,6283,sci-fi,1537094503 524 | 184,27156,anime,1537094261 525 | 184,27156,end of the world,1537094269 526 | 184,27156,epic,1537094270 527 | 184,27156,mecha,1537094278 528 | 184,27156,psychology,1537094263 529 | 184,193565,anime,1537098582 530 | 184,193565,comedy,1537098587 531 | 184,193565,gintama,1537098603 532 | 184,193565,remaster,1537098592 533 | 193,260,classic sci-fi,1435856940 534 | 193,260,space action,1435856955 535 | 193,260,space epic,1435856950 536 | 193,4878,atmospheric,1435857150 537 | 193,4878,cult film,1435857131 538 | 193,4878,dreamlike,1435857139 539 | 193,4878,hallucinatory,1435857144 540 | 193,4878,psychological,1435857147 541 | 193,4878,surreal,1435857128 542 | 193,4878,time travel,1435857135 543 | 193,7147,dreamlike,1435857017 544 | 193,7147,sentimental,1435857038 545 | 193,7147,surreal,1435857020 546 | 193,7147,Tim Burton,1435857024 547 | 193,7361,alternate reality,1435857168 548 | 193,7361,memory,1435857171 549 | 193,7361,thought-provoking,1435857163 550 | 193,97938,cinematography,1435857101 551 | 193,97938,India,1435857092 552 | 193,97938,surreal,1435857076 553 | 205,260,oldie but goodie,1519899101 554 | 205,260,sci-fi,1519899078 555 | 205,260,Star Wars,1519899108 556 | 226,6938,big wave,1160681362 557 | 226,6938,surfing,1160681362 558 | 226,48780,ummarti2006,1161615435 559 | 256,126548,funny,1447532592 560 | 256,126548,high school,1447532575 561 | 274,68319,comic book,1241762577 562 | 288,7020,Notable Nudity,1150988077 563 | 289,3,moldy,1143424860 564 | 289,3,old,1143424860 565 | 289,527,moving,1143424895 566 | 289,1101,predictable,1143424846 567 | 291,50872,a clever chef rat,1453051551 568 | 291,50872,inspirational,1453051559 569 | 291,102007,animation,1453135047 570 | 291,118696,hope,1453135079 571 | 300,6711,atmospheric,1425352343 572 | 305,4034,drugs,1462919721 573 | 305,4995,mathematics,1464428783 574 | 305,6502,zombies,1525274359 575 | 305,6953,DEPRESSING,1462398032 576 | 305,8641,stupid,1520707078 577 | 305,34405,predictable,1520284921 578 | 305,55721,Favelas,1462919777 579 | 305,83134,black comedy,1518811477 580 | 318,778,based on a book,1266408710 581 | 318,778,dark comedy,1266408707 582 | 318,778,narrated,1266408703 583 | 318,4612,reciprocal spectator,1416086512 584 | 318,4708,reciprocal spectator,1416086490 585 | 318,6400,documentary,1315333136 586 | 318,30892,Animation,1240158590 587 | 318,30892,Documentary,1240158551 588 | 318,30892,Henry Darger,1240158560 589 | 318,48698,the catholic church is the most corrupt organization in history,1276006189 590 | 318,64034,childish naivity,1242160404 591 | 318,64034,friendship,1242160427 592 | 318,68954,adventure,1266408634 593 | 318,68954,Bechdel Test:Fail,1266408641 594 | 318,68954,cartoon,1266408638 595 | 318,68954,children,1266408643 596 | 318,68954,computer animation,1266408645 597 | 318,68954,divorce,1266408656 598 | 318,68954,dogs,1266408658 599 | 318,68954,dreams,1266408662 600 | 318,68954,Pixar,1266408675 601 | 318,71494,Narrative pisstake,1426783741 602 | 318,81158,american idolatry,1299452954 603 | 318,82459,atmospheric,1300485463 604 | 318,82459,Coen Brothers,1300485477 605 | 318,82459,Jeff Bridges,1300485479 606 | 318,82459,Matt Damon,1300485480 607 | 318,82459,predictable,1300485405 608 | 318,90769,celebrity fetishism,1422524320 609 | 318,90769,mediacentralism,1422524320 610 | 318,90769,system holism,1422524320 611 | 318,96084,city politics,1421145735 612 | 318,96084,italy,1421145735 613 | 318,96084,political right versus left,1421145735 614 | 318,111364,Creature Feature,1414693918 615 | 318,118784,documentary,1420400992 616 | 318,118784,music industry,1420400992 617 | 318,118784,remix culture,1420400992 618 | 318,127172,film history,1423222565 619 | 318,127172,poetic,1423222565 620 | 318,127172,representation of children,1423222565 621 | 319,364,Disney,1461351898 622 | 319,364,Disney animated feature,1461351908 623 | 319,364,Oscar (Best Music - Original Score),1461351919 624 | 327,1288,music,1234790068 625 | 327,1923,Ben Stiller,1234789755 626 | 327,2616,comic book,1234789098 627 | 327,3481,music,1234788257 628 | 327,3897,music,1234788979 629 | 327,8641,awesome,1234789177 630 | 327,51255,british comedy,1234789115 631 | 336,1,pixar,1139045764 632 | 336,153,superhero,1139045840 633 | 336,552,knights,1139045825 634 | 336,1246,highschool,1139046768 635 | 336,32587,cult,1139046748 636 | 336,33660,boksdrama,1139046203 637 | 336,34162,stiller,1139046811 638 | 336,36529,wapendrama,1139046289 639 | 336,37729,animation,1139047294 640 | 336,38798,sisters,1139047262 641 | 341,260,ROBOTS AND ANDROIDS,1439750956 642 | 341,260,space,1439750961 643 | 356,2146,post-college,1229142995 644 | 356,37384,restaurant,1229142458 645 | 356,40955,transvestite,1229140689 646 | 356,44889,satire,1229140863 647 | 356,61323,dark comedy,1228073481 648 | 357,39,chick flick,1348627867 649 | 357,39,funny,1348627869 650 | 357,39,Paul Rudd,1348627871 651 | 357,39,quotable,1348627873 652 | 357,39,seen more than once,1348627878 653 | 357,1059,Amazing Cinematography,1348627235 654 | 357,1059,Leonardo DiCaprio,1348627233 655 | 357,1059,shakespeare,1348627264 656 | 357,1059,updated classics,1348627243 657 | 357,2762,Atmospheric,1348626902 658 | 357,2762,Bruce Willis,1348626919 659 | 357,2762,ghosts,1348626904 660 | 357,2762,imdb top 250,1348626908 661 | 357,2762,mindfuck,1348626910 662 | 357,2762,stylized,1348626915 663 | 357,2762,twist ending,1348626913 664 | 357,4370,android(s)/cyborg(s),1348628642 665 | 357,4370,artificial intelligence,1348628638 666 | 357,4370,Bittersweet,1348628628 667 | 357,4370,Steven Spielberg,1348628635 668 | 357,5464,cinematography,1348627174 669 | 357,5464,Tom Hanks,1348627179 670 | 357,7078,Bette Davis,1348628505 671 | 357,7078,Oscar (Best Actress),1348628502 672 | 357,7444,Mark Ruffalo,1348627960 673 | 357,7451,clever,1348627907 674 | 357,7451,High School,1348627902 675 | 357,7451,lesbian subtext,1348627909 676 | 357,48516,Leonardo DiCaprio,1348627149 677 | 357,48516,suspense,1348627152 678 | 357,48516,twist ending,1348627154 679 | 357,48516,undercover cop,1348627156 680 | 357,54997,Christian Bale,1348627195 681 | 357,55290,Casey Affleck,1348627116 682 | 357,55290,great performances,1348627127 683 | 357,55290,twist,1348627131 684 | 357,91500,ending,1348626941 685 | 357,91529,Anne Hathaway,1348626758 686 | 357,91529,Christian Bale,1348626755 687 | 357,91529,Christopher Nolan,1348626760 688 | 357,91529,comic book,1348626775 689 | 357,91529,great ending,1348626779 690 | 357,91529,Morgan Freeman,1348626766 691 | 357,91529,political commentary,1348626786 692 | 357,91529,superhero,1348626790 693 | 419,98961,Afghanistan,1362867327 694 | 419,98961,American propaganda,1362867290 695 | 419,98961,assassination,1362867308 696 | 419,98961,military,1362867315 697 | 419,98961,terrorism,1362867303 698 | 424,32,time travel,1457901872 699 | 424,47,mystery,1457842470 700 | 424,47,twist ending,1457842458 701 | 424,50,mindfuck,1457842328 702 | 424,50,suspense,1457842315 703 | 424,50,thriller,1457842332 704 | 424,50,tricky,1457842340 705 | 424,50,twist ending,1457842306 706 | 424,147,addiction,1457901627 707 | 424,147,heroin,1457901631 708 | 424,147,Leonardo DiCaprio,1457901625 709 | 424,147,Mark Wahlberg,1457901624 710 | 424,175,controversial,1457901665 711 | 424,175,New York City,1457901671 712 | 424,175,Nudity (Full Frontal),1457901668 713 | 424,223,cynical,1457844066 714 | 424,223,hilarious,1457844086 715 | 424,223,independent film,1457844084 716 | 424,223,quirky,1457844073 717 | 424,223,witty,1457844080 718 | 424,260,classic sci-fi,1457900772 719 | 424,260,sci-fi,1457900766 720 | 424,260,space adventure,1457900770 721 | 424,260,Star Wars,1457900775 722 | 424,288,brutality,1457846184 723 | 424,288,controversial,1457846195 724 | 424,288,dark comedy,1457846180 725 | 424,288,psychedelic,1457846191 726 | 424,288,satire,1457846198 727 | 424,288,stylized,1457846189 728 | 424,296,cult film,1457844546 729 | 424,296,drugs,1457844550 730 | 424,296,Quentin Tarantino,1457844541 731 | 424,296,Tarantino,1457844557 732 | 424,364,soundtrack,1457845497 733 | 424,527,thought-provoking,1457901041 734 | 424,541,sci-fi,1457900918 735 | 424,589,apocalypse,1457844854 736 | 424,589,Arnold Schwarzenegger,1457844841 737 | 424,589,nuclear war,1457844861 738 | 424,589,sci-fi,1457844847 739 | 424,589,Suspense,1457844864 740 | 424,589,time travel,1457844843 741 | 424,608,based on a true story,1457900882 742 | 424,608,dark comedy,1457900870 743 | 424,608,KIDNAPPING,1457900877 744 | 424,608,Steve Buscemi,1457900865 745 | 424,628,edward norton,1457843774 746 | 424,628,psychology,1457843776 747 | 424,628,suspense,1457843782 748 | 424,628,thought-provoking,1457843780 749 | 424,628,twist ending,1457843771 750 | 424,1089,ensemble cast,1457844739 751 | 424,1089,nonlinear,1457844742 752 | 424,1089,Quentin Tarantino,1457844729 753 | 424,1089,stylized,1457844737 754 | 424,1120,freedom of expression,1457845558 755 | 424,1136,british comedy,1457901498 756 | 424,1136,Monty Python,1457901496 757 | 424,1193,emotional,1457843688 758 | 424,1193,jack nicholson,1457843684 759 | 424,1198,adventure,1457901129 760 | 424,1198,archaeology,1457901464 761 | 424,1198,indiana jones,1457901132 762 | 424,1198,Steven Spielberg,1457901466 763 | 424,1198,treasure hunt,1457901471 764 | 424,1200,action,1457901258 765 | 424,1200,aliens,1457901252 766 | 424,1200,horror,1457901265 767 | 424,1200,sci-fi,1457901245 768 | 424,1200,space,1457901261 769 | 424,1200,space craft,1457901274 770 | 424,1200,SPACE TRAVEL,1457901269 771 | 424,1200,suspense,1457901256 772 | 424,1219,Alfred Hitchcock,1457902455 773 | 424,1219,psychology,1457902459 774 | 424,1219,suspenseful,1457902452 775 | 424,1219,tense,1457902461 776 | 424,1240,Action,1457901308 777 | 424,1240,artificial intelligence,1457901313 778 | 424,1240,robots,1457901305 779 | 424,1240,Sci-Fi,1457901302 780 | 424,1240,special effects,1457901322 781 | 424,1240,tense,1457901319 782 | 424,1240,time travel,1457901300 783 | 424,1258,atmospheric,1457843344 784 | 424,1258,disturbing,1457843347 785 | 424,1258,Horror,1457843352 786 | 424,1258,jack nicholson,1457843340 787 | 424,1258,masterpiece,1457843354 788 | 424,1258,psychological,1457843342 789 | 424,1258,Stanley Kubrick,1457843338 790 | 424,1258,suspense,1457843361 791 | 424,1343,horror,1457846257 792 | 424,1343,Juliette Lewis,1457846244 793 | 424,1343,Martin Scorsese,1457846252 794 | 424,1343,Robert De Niro,1457846254 795 | 424,1625,mindfuck,1457845252 796 | 424,1625,Mystery,1457845249 797 | 424,1625,plot twist,1457845255 798 | 424,1625,psychological,1457845247 799 | 424,1625,suspense,1457845258 800 | 424,1625,twist ending,1457845244 801 | 424,1704,inspirational,1457844971 802 | 424,1921,hallucinatory,1457902516 803 | 424,1921,mental illness,1457902477 804 | 424,1921,mindfuck,1457902518 805 | 424,1921,paranoid,1457902521 806 | 424,1923,Ben Stiller,1457846503 807 | 424,1923,crude humor,1457846506 808 | 424,1923,goofy,1457846511 809 | 424,2160,Atmospheric,1457843300 810 | 424,2160,creepy,1457843317 811 | 424,2160,paranoia,1457843298 812 | 424,2160,scary,1457843295 813 | 424,2160,suspense,1457843308 814 | 424,2296,SNL,1457846704 815 | 424,2329,thought-provoking,1457843729 816 | 424,2505,Nicolas Cage,1457902180 817 | 424,2571,martial arts,1457842912 818 | 424,2571,sci-fi,1457842899 819 | 424,2700,adult humor,1457844390 820 | 424,2700,controversial,1457844399 821 | 424,2700,crude humor,1457844397 822 | 424,2700,free speech,1457844402 823 | 424,2700,parody,1457844393 824 | 424,2700,satire,1457844392 825 | 424,2700,south park,1457844407 826 | 424,2700,Trey Parker,1457844412 827 | 424,2959,dark comedy,1457842797 828 | 424,2959,psychology,1457842802 829 | 424,2959,thought-provoking,1457842786 830 | 424,2959,twist ending,1457842777 831 | 424,3176,based on a book,1457845042 832 | 424,3176,creepy,1457845033 833 | 424,3176,disturbing,1457845029 834 | 424,3176,Jude Law,1457845026 835 | 424,3176,murder,1457845038 836 | 424,3176,obsession,1457845036 837 | 424,3176,psychology,1457845023 838 | 424,3176,secrets,1457845045 839 | 424,3176,serial killer,1457845031 840 | 424,3176,suspense,1457845041 841 | 424,3186,asylum,1457846005 842 | 424,3186,based on a true story,1457846002 843 | 424,3186,Brittany Murphy,1457846009 844 | 424,3186,Mental Hospital,1457846012 845 | 424,3186,psychology,1457845994 846 | 424,3186,winona ryder,1457845998 847 | 424,3499,claustrophobic,1457846281 848 | 424,3499,horror,1457846287 849 | 424,3499,scary,1457846295 850 | 424,3499,stephen king,1457846291 851 | 424,3499,suspenseful,1457846279 852 | 424,3499,tense,1457846283 853 | 424,3499,thriller,1457846285 854 | 424,3527,aliens,1457901283 855 | 424,3527,sci-fi,1457901285 856 | 424,3527,scifi cult,1457901288 857 | 424,3911,satire,1457844176 858 | 424,3948,comedy,1457846488 859 | 424,3948,Robert De Niro,1457846484 860 | 424,4226,mystery,1457842983 861 | 424,4226,psychological,1457842979 862 | 424,4226,twist ending,1457842977 863 | 424,4262,Al Pacino,1457900965 864 | 424,4816,ben stiller,1457846960 865 | 424,4816,comedy,1457846970 866 | 424,4816,David Bowie,1457846952 867 | 424,4816,goofy,1457846949 868 | 424,4816,mindless one liners,1457846956 869 | 424,4816,Will Ferrell,1457846959 870 | 424,4878,jake gyllenhaal,1457901910 871 | 424,4993,fantasy,1457901156 872 | 424,4993,high fantasy,1457901162 873 | 424,4993,Magic,1457901158 874 | 424,4993,mythology,1457901174 875 | 424,4993,tolkien,1457901165 876 | 424,4993,wizards,1457901168 877 | 424,5291,black and white,1457845331 878 | 424,5291,samurai,1457845338 879 | 424,6016,multiple storylines,1457900999 880 | 424,6016,true story,1457901005 881 | 424,6188,comedy,1457846098 882 | 424,6188,Will Ferrell,1457846100 883 | 424,6290,Rob Zombie,1457902367 884 | 424,7361,jim carrey,1457901740 885 | 424,8641,comedy,1457846537 886 | 424,8641,Will Ferrell,1457846533 887 | 424,8950,Christian Bale,1457842841 888 | 424,8950,creepy,1457842847 889 | 424,8950,powerful ending,1457842857 890 | 424,8950,psychology,1457842844 891 | 424,8950,schizophrenia,1457842854 892 | 424,8950,twist ending,1457842837 893 | 424,27020,drugs,1457901575 894 | 424,27020,lesbian,1457901570 895 | 424,30825,Ben Stiller,1457846573 896 | 424,30825,Robert De Niro,1457846569 897 | 424,34323,Rob Zombie,1457902355 898 | 424,40278,Jake Gyllenhaal,1457846443 899 | 424,40278,Modern war,1457846449 900 | 424,48516,atmospheric,1457843188 901 | 424,48516,Jack Nicholson,1457843173 902 | 424,48516,Leonardo DiCaprio,1457843178 903 | 424,48516,Martin Scorsese,1457843176 904 | 424,48516,suspense,1457843184 905 | 424,53127,based on a play,1457923334 906 | 424,53127,conspiracy theory,1457923313 907 | 424,53127,creepy,1457923337 908 | 424,53127,Insanity,1457923284 909 | 424,53127,paranoia,1457923318 910 | 424,53127,PTSD,1457923256 911 | 424,60756,funny,1457846127 912 | 424,60756,will ferrell,1457846129 913 | 424,66097,claymation,1457844468 914 | 424,66097,creepy,1457844484 915 | 424,66097,dark,1457844475 916 | 424,66097,dark fairy tale,1457844498 917 | 424,66097,Surreal,1457844481 918 | 424,66097,Tim Burton,1457844494 919 | 424,68157,black comedy,1457844667 920 | 424,68157,Brad Pitt,1457844663 921 | 424,68157,Christoph Waltz,1457844660 922 | 424,68157,Quentin Tarantino,1457844652 923 | 424,68157,satire,1457844670 924 | 424,74458,insanity,1457843102 925 | 424,74458,Leonardo DiCaprio,1457843110 926 | 424,74458,Martin Scorsese,1457843107 927 | 424,74458,plot twist,1457843104 928 | 424,74458,psychological,1457843098 929 | 424,74458,Psychological Thriller,1457843113 930 | 424,74458,thought-provoking,1457843122 931 | 424,79132,action,1457844927 932 | 424,79132,alternate reality,1457844911 933 | 424,79132,Leonardo DiCaprio,1457844916 934 | 424,79132,sci-fi,1457844903 935 | 424,79132,thought-provoking,1457844920 936 | 424,79132,visually appealing,1457844914 937 | 424,81562,stranded,1457901404 938 | 424,81591,alter ego,1457845684 939 | 424,81591,atmospheric,1457845658 940 | 424,81591,creepy,1457845668 941 | 424,81591,horror,1457845670 942 | 424,81591,obsession,1457845677 943 | 424,81591,surreal,1457845661 944 | 424,102903,illusions,1457845623 945 | 424,102903,overcomplicated,1457845593 946 | 424,102903,predictable,1457845596 947 | 424,102903,stupid ending,1457845587 948 | 424,104879,absorbing,1457846360 949 | 424,104879,acting,1457846358 950 | 424,104879,atmospheric,1457846366 951 | 424,104879,Hugh Jackman,1457846364 952 | 424,104879,Jake Gyllenhaal,1457846355 953 | 424,104879,morality,1457846368 954 | 424,104879,mystery,1457846378 955 | 424,104879,thriller,1457846362 956 | 424,106100,Jared Leto,1457845924 957 | 424,106100,social commentary,1457845921 958 | 424,106100,uplifting,1457845933 959 | 424,106489,fantasy,1457901210 960 | 424,106489,Tolkien,1457901214 961 | 424,106489,too long,1457901201 962 | 424,109487,Christopher Nolan,1457901805 963 | 424,109487,sci-fi,1457901808 964 | 424,109487,time-travel,1457901811 965 | 424,112515,atmospheric,1457843258 966 | 424,112515,dark,1457843256 967 | 424,112515,Metaphorical,1457843228 968 | 424,112515,psychological,1457843261 969 | 424,112556,annoying,1457845088 970 | 424,112556,Stupid ending,1457845155 971 | 435,750,dark comedy,1366676044 972 | 435,2959,dark comedy,1366676088 973 | 435,58559,psychology,1366676040 974 | 435,58559,superhero,1366676054 975 | 439,5952,Myth,1436944038 976 | 439,98809,Action,1436944128 977 | 439,98809,Myth,1436944141 978 | 462,152711,Cambodia,1478489688 979 | 462,152711,crime,1478489709 980 | 462,152711,human rights,1478489696 981 | 462,152711,murder,1478489701 982 | 462,152711,procedural,1478489713 983 | 474,1,pixar,1137206825 984 | 474,2,game,1137375552 985 | 474,5,pregnancy,1137373903 986 | 474,5,remake,1137373903 987 | 474,7,remake,1137375642 988 | 474,11,politics,1137374904 989 | 474,11,president,1137374904 990 | 474,14,politics,1137375623 991 | 474,14,president,1137375623 992 | 474,16,Mafia,1137181640 993 | 474,17,Jane Austen,1137181153 994 | 474,21,Hollywood,1137206178 995 | 474,22,serial killer,1137375496 996 | 474,25,alcoholism,1138137555 997 | 474,26,Shakespeare,1138137603 998 | 474,28,In Netflix queue,1137201942 999 | 474,28,Jane Austen,1137180037 1000 | 474,29,kidnapping,1138137473 1001 | 474,31,high school,1137375502 1002 | 474,31,teacher,1137375502 1003 | 474,32,time travel,1137206826 1004 | 474,34,Animal movie,1137180956 1005 | 474,34,pigs,1137205237 1006 | 474,36,death penalty,1137202363 1007 | 474,36,Nun,1137191227 1008 | 474,38,twins,1137373760 1009 | 474,39,Emma,1138137492 1010 | 474,39,Jane Austen,1138137492 1011 | 474,40,In Netflix queue,1137202107 1012 | 474,40,South Africa,1137202107 1013 | 474,41,Shakespeare,1138137646 1014 | 474,43,England,1138137640 1015 | 474,45,Journalism,1137206826 1016 | 474,46,wedding,1137375539 1017 | 474,47,serial killer,1137206452 1018 | 474,50,heist,1137206826 1019 | 474,52,adoption,1137206315 1020 | 474,52,prostitution,1137206371 1021 | 474,58,writing,1138137613 1022 | 474,62,music,1137374957 1023 | 474,92,Jekyll and Hyde,1138137574 1024 | 474,96,theater,1138137537 1025 | 474,101,crime,1138137460 1026 | 474,104,golf,1137374014 1027 | 474,107,muppets,1137375573 1028 | 474,110,Scotland,1137180974 1029 | 474,111,assassination,1137206513 1030 | 474,116,Holocaust,1138040398 1031 | 474,122,dating,1137373679 1032 | 474,140,journalism,1137375031 1033 | 474,150,moon,1137205234 1034 | 474,150,NASA,1137180977 1035 | 474,150,space,1137205233 1036 | 474,153,superhero,1137375453 1037 | 474,160,Michael Crichton,1137373726 1038 | 474,161,submarine,1138137497 1039 | 474,162,In Netflix queue,1137202038 1040 | 474,185,computers,1137375604 1041 | 474,199,Made me cry,1137203124 1042 | 474,215,generation X,1138040410 1043 | 474,216,school,1137373675 1044 | 474,222,Ireland,1137181644 1045 | 474,223,generation X,1138137480 1046 | 474,224,mental illness,1138137516 1047 | 474,224,psychology,1138137516 1048 | 474,230,Stephen King,1138137506 1049 | 474,232,In Netflix queue,1137201027 1050 | 474,235,movie business,1137181651 1051 | 474,237,basketball,1138137528 1052 | 474,237,France,1138137528 1053 | 474,237,infertility,1138137528 1054 | 474,246,basketball,1137784780 1055 | 474,247,Australia,1137181666 1056 | 474,249,Beethoven,1137181672 1057 | 474,252,Einstein,1137374021 1058 | 474,257,court,1137375555 1059 | 474,260,darth vader,1137206494 1060 | 474,260,luke skywalker,1137206496 1061 | 474,260,space opera,1137206504 1062 | 474,261,Louisa May Alcott,1137181688 1063 | 474,262,England,1137206203 1064 | 474,262,Girl Power,1137206203 1065 | 474,262,India,1137206203 1066 | 474,272,England,1138137566 1067 | 474,272,mental illness,1138137566 1068 | 474,277,Christmas,1137374027 1069 | 474,279,In Netflix queue,1137202159 1070 | 474,280,prison,1137375585 1071 | 474,282,disability,1137375594 1072 | 474,282,twins,1137375594 1073 | 474,290,In Netflix queue,1137201907 1074 | 474,293,hit men,1138137619 1075 | 474,296,hit men,1137205001 1076 | 474,300,TV,1137206376 1077 | 474,307,Death,1137203087 1078 | 474,308,marriage,1137206827 1079 | 474,316,time travel,1138137673 1080 | 474,317,Christmas,1137374047 1081 | 474,318,prison,1137205060 1082 | 474,318,Stephen King,1137181171 1083 | 474,318,wrongful imprisonment,1137205064 1084 | 474,326,In Netflix queue,1137201797 1085 | 474,329,Enterprise,1137206491 1086 | 474,337,mental illness,1137206828 1087 | 474,338,serial killer,1137375662 1088 | 474,339,coma,1138137781 1089 | 474,342,Australia,1138137593 1090 | 474,342,weddings,1138137593 1091 | 474,345,cross dressing,1138040381 1092 | 474,345,men in drag,1138040381 1093 | 474,345,remade,1138040381 1094 | 474,349,Tom Clancy,1137180961 1095 | 474,350,John Grisham,1137375482 1096 | 474,351,interracial romance,1137374008 1097 | 474,356,shrimp,1137375519 1098 | 474,356,Vietnam,1137375519 1099 | 474,357,wedding,1137375527 1100 | 474,361,gambling,1138137551 1101 | 474,363,Holocaust,1137202123 1102 | 474,363,In Netflix queue,1137202123 1103 | 474,364,Disney,1137180959 1104 | 474,371,journalism,1137375629 1105 | 474,377,bus,1137206476 1106 | 474,380,spies,1137206826 1107 | 474,381,alcoholism,1137375043 1108 | 474,412,Edith Wharton,1137181633 1109 | 474,421,horses,1138040415 1110 | 474,425,mental illness,1138040434 1111 | 474,425,Oscar (Best Actress),1138040434 1112 | 474,440,President,1137191329 1113 | 474,454,John Grisham,1137181658 1114 | 474,457,based on a TV show,1137784717 1115 | 474,471,hula hoop,1137375547 1116 | 474,474,assassination,1138137543 1117 | 474,475,Ireland,1137181677 1118 | 474,477,biopic,1138137776 1119 | 474,480,Dinosaur,1137202921 1120 | 474,488,Japan,1137375570 1121 | 474,488,sexuality,1137375570 1122 | 474,497,Shakespeare,1137181143 1123 | 474,500,cross dressing,1137374959 1124 | 474,500,divorce,1137374961 1125 | 474,500,men in drag,1137374967 1126 | 474,508,AIDs,1137375634 1127 | 474,513,radio,1138137637 1128 | 474,513,show business,1138137637 1129 | 474,515,Butler,1137202599 1130 | 474,515,Housekeeper,1137202599 1131 | 474,516,military,1137374250 1132 | 474,522,Australia,1138137660 1133 | 474,522,racism,1138137660 1134 | 474,522,violence,1138137660 1135 | 474,524,football,1137375002 1136 | 474,527,Holocaust,1137180970 1137 | 474,529,chess,1137181705 1138 | 474,531,In Netflix queue,1137201896 1139 | 474,534,C.S. Lewis,1137181163 1140 | 474,535,large cast,1137206471 1141 | 474,538,race,1138137665 1142 | 474,539,Empire State Building,1137203041 1143 | 474,541,robots,1137375464 1144 | 474,543,beat poetry,1137203057 1145 | 474,551,Christmas,1137375615 1146 | 474,551,Halloween,1137375615 1147 | 474,556,politics,1137375666 1148 | 474,562,adolescence,1137206827 1149 | 474,586,christmas,1137374182 1150 | 474,587,overrated,1137180951 1151 | 474,588,Disney,1137180971 1152 | 474,589,robots,1137206517 1153 | 474,590,American Indians,1137180976 1154 | 474,590,Native Americans,1137180976 1155 | 474,592,superhero,1137206164 1156 | 474,593,Hannibal Lector,1137180982 1157 | 474,594,Disney,1137375648 1158 | 474,595,Disney,1137180963 1159 | 474,596,Disney,1137374224 1160 | 474,597,prostitution,1137206324 1161 | 474,608,Coen Brothers,1137180968 1162 | 474,616,Disney,1138040401 1163 | 474,628,priest,1137375884 1164 | 474,638,babies,1137375798 1165 | 474,647,Gulf War,1137375718 1166 | 474,648,based on a TV show,1138137584 1167 | 474,668,India,1138137607 1168 | 474,670,India,1137181320 1169 | 474,671,spoof,1138137597 1170 | 474,673,Bugs Bunny,1137373978 1171 | 474,708,Veterinarian,1137203107 1172 | 474,720,Aardman,1137206828 1173 | 474,728,In Netflix queue,1137202096 1174 | 474,733,Alcatraz,1137206418 1175 | 474,736,Disaster,1137180960 1176 | 474,745,Aardman,1137206827 1177 | 474,748,aliens,1138137820 1178 | 474,750,Atomic bomb,1137202888 1179 | 474,778,drug abuse,1137191490 1180 | 474,780,aliens,1137206181 1181 | 474,800,In Netflix queue,1137201974 1182 | 474,805,John Grisham,1137375969 1183 | 474,818,based on a TV show,1137375996 1184 | 474,830,adultery,1137374123 1185 | 474,832,kidnapping,1137374242 1186 | 474,838,Jane Austen,1137375748 1187 | 474,852,golf,1137374262 1188 | 474,858,Mafia,1137180955 1189 | 474,892,Shakespeare,1137375988 1190 | 474,898,divorce,1137206322 1191 | 474,899,movie business,1137181713 1192 | 474,900,France,1138137809 1193 | 474,902,Capote,1138137849 1194 | 474,903,falling,1137205255 1195 | 474,904,voyeurism,1137206380 1196 | 474,905,Screwball,1137202499 1197 | 474,906,Brooch,1137191231 1198 | 474,907,divorce,1137375780 1199 | 474,908,Mount Rushmore,1138137968 1200 | 474,909,adultery,1137206162 1201 | 474,910,men in drag,1137191481 1202 | 474,911,heist,1138306780 1203 | 474,912,start of a beautiful friendship,1137202319 1204 | 474,913,statue,1137206229 1205 | 474,914,George Bernard Shaw,1137202973 1206 | 474,915,rich guy - poor girl,1137206449 1207 | 474,916,Italy,1137206429 1208 | 474,916,royalty,1137206429 1209 | 474,918,1900s,1138137949 1210 | 474,919,Dorothy,1137205270 1211 | 474,919,Toto,1137205270 1212 | 474,920,Civil War,1137374145 1213 | 474,921,television,1137375857 1214 | 474,922,movies,1137207406 1215 | 474,923,Rosebud,1137202868 1216 | 474,924,Hal,1137368035 1217 | 474,924,space,1137368035 1218 | 474,926,Hollywood,1137202821 1219 | 474,927,divorce,1137368140 1220 | 474,928,Mrs. DeWinter,1137202590 1221 | 474,929,Europe,1140441887 1222 | 474,929,journalism,1140441887 1223 | 474,929,war,1140441887 1224 | 474,930,assassination,1137207035 1225 | 474,931,amnesia,1138137999 1226 | 474,934,wedding,1137375767 1227 | 474,936,Cold War,1137521228 1228 | 474,936,Russia,1137521228 1229 | 474,938,prostitution,1137374937 1230 | 474,940,swashbuckler,1138137798 1231 | 474,941,swashbuckler,1138137943 1232 | 474,943,ghosts,1138306878 1233 | 474,944,Shangri-La,1137207008 1234 | 474,945,Astaire and Rogers,1137181301 1235 | 474,947,butler,1137521096 1236 | 474,947,homeless,1137521096 1237 | 474,947,screwball,1137521103 1238 | 474,948,oil,1137206938 1239 | 474,950,Nick and Nora Charles,1137200994 1240 | 474,951,Screwball,1137202906 1241 | 474,952,race,1137375685 1242 | 474,953,Christmas,1138137927 1243 | 474,954,Politics,1137202956 1244 | 474,955,leopard,1137368052 1245 | 474,955,screwball,1137368052 1246 | 474,956,adoption,1137207058 1247 | 474,965,fugitive,1137206861 1248 | 474,968,zombies,1137181787 1249 | 474,969,missionary,1137202249 1250 | 474,970,crime,1138306770 1251 | 474,971,Tennessee Williams,1138137870 1252 | 474,973,journalism,1138306956 1253 | 474,976,Hemingway,1137375762 1254 | 474,986,Animal movie,1137181755 1255 | 474,991,Ireland,1137375821 1256 | 474,994,food,1138137832 1257 | 474,1006,death penalty,1138137880 1258 | 474,1006,John Grisham,1138137880 1259 | 474,1010,Disney,1137375809 1260 | 474,1010,race,1137375809 1261 | 474,1013,twins,1137375864 1262 | 474,1022,Disney,1137181749 1263 | 474,1025,Disney,1137375959 1264 | 474,1025,King Arthur,1137375959 1265 | 474,1028,Disney,1137375815 1266 | 474,1028,nanny,1137375815 1267 | 474,1029,Disney,1137375741 1268 | 474,1030,Disney,1137374219 1269 | 474,1032,Disney,1137374073 1270 | 474,1033,Disney,1137202900 1271 | 474,1035,Rogers and Hammerstein,1137181179 1272 | 474,1041,In Netflix queue,1137201463 1273 | 474,1042,Music,1137203070 1274 | 474,1059,Shakespeare,1137181313 1275 | 474,1066,Astaire and Rogers,1137181830 1276 | 474,1068,anti-Semitism,1138306851 1277 | 474,1076,governess,1137521394 1278 | 474,1079,fish,1137202895 1279 | 474,1080,Bible,1137375841 1280 | 474,1080,parody,1137375841 1281 | 474,1080,religion,1137375841 1282 | 474,1081,cross dressing,1138138013 1283 | 474,1082,politics,1138137861 1284 | 474,1084,1920s,1138137844 1285 | 474,1084,gangsters,1138137844 1286 | 474,1088,dance,1138306856 1287 | 474,1088,music,1138306856 1288 | 474,1089,heist,1137207100 1289 | 474,1089,violence,1137207100 1290 | 474,1090,Vietnam,1138137990 1291 | 474,1093,1960s,1137375736 1292 | 474,1093,Jim Morrison,1137375736 1293 | 474,1093,music,1137375736 1294 | 474,1096,Holocaust,1137375015 1295 | 474,1097,aliens,1137521204 1296 | 474,1101,Navy,1138138004 1297 | 474,1103,1950s,1137207088 1298 | 474,1103,adolescence,1137207088 1299 | 474,1104,Tennessee Williams,1137207146 1300 | 474,1124,aging,1137207041 1301 | 474,1125,Clousseau,1137375898 1302 | 474,1135,military,1137374233 1303 | 474,1136,England,1137202949 1304 | 474,1136,King Arthur,1137202949 1305 | 474,1147,boxing,1137201093 1306 | 474,1147,In Netflix queue,1137201093 1307 | 474,1148,Aardman,1137368133 1308 | 474,1171,politics,1137181738 1309 | 474,1177,Italy,1137191228 1310 | 474,1178,court,1137521169 1311 | 474,1178,military,1137521169 1312 | 474,1179,crime,1138306883 1313 | 474,1183,adultery,1137374103 1314 | 474,1185,In Netflix queue,1137201055 1315 | 474,1187,disability,1137207053 1316 | 474,1188,Australia,1137202649 1317 | 474,1188,dance,1137202649 1318 | 474,1189,In Netflix queue,1137201133 1319 | 474,1189,police,1137201133 1320 | 474,1193,mental illness,1138137985 1321 | 474,1196,I am your father,1137202639 1322 | 474,1196,space,1137205077 1323 | 474,1196,space opera,1137205075 1324 | 474,1197,Inigo Montoya,1137202999 1325 | 474,1197,six-fingered man,1137202999 1326 | 474,1198,archaeology,1137784496 1327 | 474,1198,ark of the covenant,1137207066 1328 | 474,1198,indiana jones,1137207064 1329 | 474,1200,space,1138137801 1330 | 474,1201,spaghetti western,1138137912 1331 | 474,1203,court,1137206845 1332 | 474,1204,Middle East,1137206997 1333 | 474,1206,brainwashing,1138137889 1334 | 474,1207,Harper Lee,1137191542 1335 | 474,1207,racism,1137191542 1336 | 474,1208,Vietnam,1138137816 1337 | 474,1209,spaghetti western,1138137976 1338 | 474,1210,darth vader,1137207127 1339 | 474,1210,luke skywalker,1137207127 1340 | 474,1210,space opera,1137207127 1341 | 474,1212,ferris wheel,1137201067 1342 | 474,1212,Venice,1143683500 1343 | 474,1212,zither,1143683500 1344 | 474,1213,Mafia,1138137920 1345 | 474,1214,aliens,1137766234 1346 | 474,1217,samurai,1137207077 1347 | 474,1219,Norman Bates,1137203028 1348 | 474,1220,Saturday Night Live,1137375705 1349 | 474,1221,Mafia,1137181066 1350 | 474,1222,Vietnam,1137181761 1351 | 474,1223,moon,1137205261 1352 | 474,1224,Shakespeare,1137181778 1353 | 474,1225,Mozart,1137181247 1354 | 474,1225,Salieri,1137181247 1355 | 474,1228,boxing,1137375891 1356 | 474,1230,New York,1137206872 1357 | 474,1231,NASA,1137181802 1358 | 474,1231,space,1137207104 1359 | 474,1233,submarine,1138137835 1360 | 474,1234,The Entertainer,1137520998 1361 | 474,1235,May-December romance,1137206961 1362 | 474,1237,chess,1137375008 1363 | 474,1237,death,1137375008 1364 | 474,1240,robots,1137368088 1365 | 474,1242,Civil War,1137181272 1366 | 474,1243,Shakespeare sort of,1137181815 1367 | 474,1244,black and white,1138306904 1368 | 474,1245,Mafia,1137375832 1369 | 474,1246,High School,1137191334 1370 | 474,1247,Simon and Garfunkel,1137181084 1371 | 474,1249,hit men,1138137899 1372 | 474,1250,POW,1137206891 1373 | 474,1252,incest,1137521080 1374 | 474,1253,aliens,1137206910 1375 | 474,1254,Cold,1137203094 1376 | 474,1257,skiing,1137375698 1377 | 474,1258,Stephen King,1137181824 1378 | 474,1259,Stephen King,1137181838 1379 | 474,1260,serial killer,1137207017 1380 | 474,1262,POW,1137206951 1381 | 474,1263,Vietnam,1137375724 1382 | 474,1266,revenge,1138031666 1383 | 474,1267,assassination,1137207027 1384 | 474,1267,brainwashing,1137207027 1385 | 474,1269,murder,1138137828 1386 | 474,1270,time travel,1137202832 1387 | 474,1272,World War II,1137181795 1388 | 474,1276,prison,1137520930 1389 | 474,1277,In Netflix queue,1137201859 1390 | 474,1278,spoof,1138031675 1391 | 474,1280,In Netflix queue,1137201087 1392 | 474,1281,Nazis,1137206944 1393 | 474,1282,Disney,1137375752 1394 | 474,1283,gunfight,1138306895 1395 | 474,1284,Hammett,1138306774 1396 | 474,1285,high school,1137368062 1397 | 474,1288,heavy metal,1137368105 1398 | 474,1288,mockumentary,1137368105 1399 | 474,1288,music,1137368105 1400 | 474,1291,archaeology,1137206982 1401 | 474,1291,Holy Grail,1137206982 1402 | 474,1292,television,1137206878 1403 | 474,1293,India,1137191587 1404 | 474,1296,E. M. Forster,1137191679 1405 | 474,1299,Cambodia,1138137933 1406 | 474,1299,Vietnam,1138137933 1407 | 474,1301,Shakespeare sort of,1138306870 1408 | 474,1301,space,1138306870 1409 | 474,1302,baseball,1138137903 1410 | 474,1303,India,1138031736 1411 | 474,1304,crime,1138137854 1412 | 474,1307,New York,1137202686 1413 | 474,1333,birds,1137202841 1414 | 474,1343,remake,1137181261 1415 | 474,1344,lawyer,1137202856 1416 | 474,1345,high school,1137784649 1417 | 474,1345,prom,1137784649 1418 | 474,1345,Stephen King,1137784649 1419 | 474,1348,vampires,1137374972 1420 | 474,1350,demons,1137368289 1421 | 474,1353,personals ads,1137202938 1422 | 474,1354,religion,1137202850 1423 | 474,1356,Borg,1137202633 1424 | 474,1358,ex-con,1137203050 1425 | 474,1361,In Netflix queue,1137201059 1426 | 474,1363,religion,1137374986 1427 | 474,1366,Arthur Miller,1137191572 1428 | 474,1367,dogs,1137374063 1429 | 474,1367,remake,1137374063 1430 | 474,1372,Klingons,1137203064 1431 | 474,1374,Captain Kirk,1138031870 1432 | 474,1376,whales,1137368320 1433 | 474,1377,superhero,1137374089 1434 | 474,1380,high school,1137368225 1435 | 474,1381,high school,1137374154 1436 | 474,1387,Shark,1137202913 1437 | 474,1389,shark,1137373926 1438 | 474,1393,sports,1137205489 1439 | 474,1394,Coen Brothers,1137181415 1440 | 474,1396,spying,1138031853 1441 | 474,1398,Hemingway,1137374949 1442 | 474,1407,slasher,1138031814 1443 | 474,1407,spoof,1138031814 1444 | 474,1408,Hawkeye,1137368264 1445 | 474,1408,James Fennimore Cooper,1137368264 1446 | 474,1411,Shakespeare,1137191597 1447 | 474,1413,Conan,1137205643 1448 | 474,1416,Andrew Lloyd Weber,1137374117 1449 | 474,1423,In Netflix queue,1137202085 1450 | 474,1423,Vietnam,1137202085 1451 | 474,1441,mental illness,1137375694 1452 | 474,1446,In Netflix queue,1137201854 1453 | 474,1449,mockumentary,1137368329 1454 | 474,1466,Mafia,1137191577 1455 | 474,1474,jungle,1137374191 1456 | 474,1485,lawyer,1137375802 1457 | 474,1496,Tolstoy,1137191560 1458 | 474,1500,hit men,1137368241 1459 | 474,1500,reunion,1137368241 1460 | 474,1513,reunion,1137375929 1461 | 474,1537,ballroom dance,1137368308 1462 | 474,1542,In Netflix queue,1137202048 1463 | 474,1544,dinosaurs,1137373781 1464 | 474,1545,death,1138031807 1465 | 474,1569,weddings,1138031775 1466 | 474,1573,transplants,1137205452 1467 | 474,1580,aliens,1137205519 1468 | 474,1586,military,1137374138 1469 | 474,1608,president,1137375681 1470 | 474,1610,Tom Clancy,1137191607 1471 | 474,1617,Police,1137191242 1472 | 474,1625,twist ending,1138031717 1473 | 474,1635,1970s,1137191612 1474 | 474,1643,England,1137375849 1475 | 474,1643,Queen Victoria,1137375849 1476 | 474,1645,lawyers,1137374095 1477 | 474,1650,Henry James,1138031883 1478 | 474,1653,future,1137205460 1479 | 474,1663,military,1137375949 1480 | 474,1672,In Netflix queue,1137202154 1481 | 474,1674,Amish,1137521041 1482 | 474,1680,alternate universe,1138031846 1483 | 474,1682,TV,1137205617 1484 | 474,1683,Henry James,1137181445 1485 | 474,1711,Savannah,1137374207 1486 | 474,1717,sequel,1138031833 1487 | 474,1717,slasher,1138031824 1488 | 474,1717,spoof,1138031824 1489 | 474,1719,Canada,1139838573 1490 | 474,1719,death,1139838573 1491 | 474,1721,shipwreck,1138031879 1492 | 474,1732,Coen Brothers,1138306661 1493 | 474,1735,Charles Dickens,1137374163 1494 | 474,1748,amnesia,1138031699 1495 | 474,1777,1980s,1137205630 1496 | 474,1797,In Netflix queue,1137201971 1497 | 474,1810,politics,1137374994 1498 | 474,1834,twist ending,1138031861 1499 | 474,1836,No DVD at Netflix,1137179444 1500 | 474,1893,In Netflix queue,1137201105 1501 | 474,1900,In Netflix queue,1137201889 1502 | 474,1902,generation X,1138031706 1503 | 474,1909,conspiracy,1137205667 1504 | 474,1912,heist,1138031795 1505 | 474,1913,Australia,1137191654 1506 | 474,1917,space,1137373862 1507 | 474,1921,mathematics,1138031797 1508 | 474,1931,ships,1138031771 1509 | 474,1938,alcoholism,1137766211 1510 | 474,1940,anti-Semitism,1137368219 1511 | 474,1940,Judaism,1137368219 1512 | 474,1942,Huey Long,1137201047 1513 | 474,1942,In Netflix queue,1137201047 1514 | 474,1942,Robert Penn Warren,1137201047 1515 | 474,1945,boxing,1137191644 1516 | 474,1945,coulda been a contender,1137191644 1517 | 474,1947,Gangs,1137202674 1518 | 474,1950,racism,1137520942 1519 | 474,1951,Dickens,1137191634 1520 | 474,1952,prostitution,1138031755 1521 | 474,1953,drugs,1137368199 1522 | 474,1953,police,1137368199 1523 | 474,1954,boxing,1137191671 1524 | 474,1954,sports,1137191671 1525 | 474,1955,divorce,1137181397 1526 | 474,1957,Olympics,1137181069 1527 | 474,1957,sports,1137181069 1528 | 474,1959,adultery,1138031923 1529 | 474,1959,Africa,1138031923 1530 | 474,1960,China,1137368246 1531 | 474,1961,autism,1137191663 1532 | 474,1962,rasicm,1137368186 1533 | 474,1975,Jason,1137374129 1534 | 474,1976,Jason,1137373611 1535 | 474,1977,Jason,1137373908 1536 | 474,1978,Jason,1137373605 1537 | 474,1979,Jason,1137373625 1538 | 474,1983,halloween,1137374175 1539 | 474,1984,halloween,1137374180 1540 | 474,1994,ghosts,1138031804 1541 | 474,1995,ghosts,1137374228 1542 | 474,1996,ghosts,1137373825 1543 | 474,1997,demons,1137368191 1544 | 474,2006,California,1138031745 1545 | 474,2006,Mexico,1138031745 1546 | 474,2011,time travel,1137374084 1547 | 474,2019,samurai,1137368312 1548 | 474,2020,adultery,1137368176 1549 | 474,2022,Bible,1137202075 1550 | 474,2022,Katzanzakis,1143683469 1551 | 474,2028,World War II,1137180952 1552 | 474,2054,Disney,1138032013 1553 | 474,2058,police,1138032116 1554 | 474,2059,twins,1137374214 1555 | 474,2064,General Motors,1138032156 1556 | 474,2064,Michigan,1138032156 1557 | 474,2065,In Netflix queue,1137202057 1558 | 474,2066,memory,1138032133 1559 | 474,2070,music,1137191821 1560 | 474,2071,AIDs,1137200442 1561 | 474,2071,In Netflix queue,1137200442 1562 | 474,2076,sexuality,1137418851 1563 | 474,2076,suburbia,1137418851 1564 | 474,2078,Disney,1138032023 1565 | 474,2080,Disney,1138032086 1566 | 474,2085,Disney,1137191781 1567 | 474,2097,carnival,1138559451 1568 | 474,2097,Ray Bradbury,1138559451 1569 | 474,2100,mermaid,1137191808 1570 | 474,2108,weather forecaster,1138032081 1571 | 474,2114,S.E. Hinton,1137191789 1572 | 474,2115,archaeology,1137784519 1573 | 474,2116,Tolkein,1137374427 1574 | 474,2118,Stephen King,1137191728 1575 | 474,2121,Stephen King,1137374355 1576 | 474,2122,Stephen King,1137373696 1577 | 474,2139,mice,1137521184 1578 | 474,2145,1980s,1138032148 1579 | 474,2145,high school,1138032148 1580 | 474,2160,demons,1138032159 1581 | 474,2167,vampires,1137374320 1582 | 474,2171,aquarium,1137202573 1583 | 474,2171,Boston,1137202573 1584 | 474,2171,personals ads,1137202573 1585 | 474,2186,murder,1137205604 1586 | 474,2187,theater,1140467428 1587 | 474,2194,Capone,1138032218 1588 | 474,2194,gangsters,1138032218 1589 | 474,2202,survival,1137205513 1590 | 474,2203,small towns,1138032173 1591 | 474,2206,paranoia,1137205610 1592 | 474,2208,train,1137271242 1593 | 474,2243,journalism,1137205380 1594 | 474,2247,Mafia,1137374432 1595 | 474,2248,In Your Eyes,1137202613 1596 | 474,2248,Lloyd Dobbler,1137202613 1597 | 474,2249,Mafia,1137374445 1598 | 474,2268,court,1137191761 1599 | 474,2268,military,1137191761 1600 | 474,2291,freaks,1137205437 1601 | 474,2295,Screwball,1137202461 1602 | 474,2300,Broadway,1137205562 1603 | 474,2302,court,1137374439 1604 | 474,2310,adolescence,1138032095 1605 | 474,2312,death penalty,1137374919 1606 | 474,2313,freaks,1137205442 1607 | 474,2324,Holocaust,1137181119 1608 | 474,2329,racism,1138031951 1609 | 474,2334,terrorism,1138032178 1610 | 474,2335,football,1137374560 1611 | 474,2336,England,1137191737 1612 | 474,2351,In Netflix queue,1137202131 1613 | 474,2355,Pixar,1137191711 1614 | 474,2357,South America,1137205389 1615 | 474,2360,In Netflix queue,1137201464 1616 | 474,2390,In Netflix queue,1137202019 1617 | 474,2391,heist,1138032185 1618 | 474,2394,Bible,1137191285 1619 | 474,2394,Moses,1137202582 1620 | 474,2396,England,1137191800 1621 | 474,2410,boxing,1137374485 1622 | 474,2411,boxing,1137373840 1623 | 474,2412,boxing,1137373964 1624 | 474,2420,martial arts,1138032051 1625 | 474,2421,martial arts,1137374406 1626 | 474,2422,martial arts,1137374411 1627 | 474,2423,christmas,1137373883 1628 | 474,2424,e-mail,1137202717 1629 | 474,2424,remake,1137202717 1630 | 474,2431,doctors,1137373812 1631 | 474,2442,In Netflix queue,1137201903 1632 | 474,2463,kidnapping,1137374497 1633 | 474,2467,religion,1140703344 1634 | 474,2470,Australia,1137374348 1635 | 474,2471,Australia,1137373731 1636 | 474,2491,food,1137374514 1637 | 474,2494,Holocaust,1137179426 1638 | 474,2494,Hungary,1137179426 1639 | 474,2502,stapler,1138032123 1640 | 474,2502,workplace,1138032129 1641 | 474,2506,disability,1137374460 1642 | 474,2517,Stephen King,1137373705 1643 | 474,2529,twist ending,1138032140 1644 | 474,2550,ghosts,1138032008 1645 | 474,2553,evil children,1138038893 1646 | 474,2554,evil children,1138038892 1647 | 474,2565,Siam,1137191238 1648 | 474,2571,alternate universe,1137204991 1649 | 474,2572,Shakespeare sort of,1137374893 1650 | 474,2579,black and white,1138032056 1651 | 474,2581,high school,1138032122 1652 | 474,2583,southern US,1138031988 1653 | 474,2598,golfing,1137374470 1654 | 474,2600,virtual reality,1138031996 1655 | 474,2612,motherhood,1138032112 1656 | 474,2628,prequel,1138032197 1657 | 474,2628,the Force,1138032197 1658 | 474,2640,superhero,1138032200 1659 | 474,2641,superhero,1138032201 1660 | 474,2642,superhero,1137373986 1661 | 474,2648,In Netflix queue,1137200966 1662 | 474,2660,aliens,1137521194 1663 | 474,2664,1950s,1138032017 1664 | 474,2677,In Netflix queue,1137202023 1665 | 474,2692,alternate endings,1138032166 1666 | 474,2693,Star Trek,1138032207 1667 | 474,2699,spiders,1137373650 1668 | 474,2710,claims to be true,1138031976 1669 | 474,2710,video,1138031976 1670 | 474,2712,sexuality,1138032003 1671 | 474,2716,ghosts,1137205464 1672 | 474,2717,Ghosts,1137373633 1673 | 474,2719,remake,1137373916 1674 | 474,2723,superhero,1137205533 1675 | 474,2726,heist,1137205507 1676 | 474,2728,crucifixion,1137520905 1677 | 474,2728,Rome,1137520896 1678 | 474,2728,slavery,1137520896 1679 | 474,2745,Missionary,1137202541 1680 | 474,2745,Priest,1137191266 1681 | 474,2750,nostalgia,1137443255 1682 | 474,2750,radio,1137443255 1683 | 474,2761,robots,1138032019 1684 | 474,2762,ghosts,1137205068 1685 | 474,2762,I see dead people,1137202627 1686 | 474,2787,Stephen King,1137374342 1687 | 474,2791,aviation,1138031941 1688 | 474,2791,spoof,1138031941 1689 | 474,2797,children,1138032265 1690 | 474,2801,gambling,1137205541 1691 | 474,2803,lawyers,1137374982 1692 | 474,2804,Christmas,1137191720 1693 | 474,2827,aliens,1137374307 1694 | 474,2863,Beatles,1138032352 1695 | 474,2871,river,1138032293 1696 | 474,2872,England,1137191745 1697 | 474,2872,King Arthur,1137191745 1698 | 474,2905,In Netflix queue,1137201967 1699 | 474,2915,prostitution,1137374478 1700 | 474,2918,High School,1137191753 1701 | 474,2919,Academy award (Best Supporting Actress),1137367916 1702 | 474,2919,Indonesia,1137367916 1703 | 474,2919,journalism,1137367916 1704 | 474,2927,adultery,1138032272 1705 | 474,2935,gambling,1138032373 1706 | 474,2937,screwball,1137205550 1707 | 474,2939,In Netflix queue,1137202140 1708 | 474,2940,nightclub,1138032341 1709 | 474,2941,island,1138032457 1710 | 474,2943,Vietnam,1138804253 1711 | 474,2952,gambling,1137191914 1712 | 474,2959,violence,1137205034 1713 | 474,2966,brothers,1138032507 1714 | 474,2966,lawn mower,1138032507 1715 | 474,2967,evil children,1138038912 1716 | 474,2968,time travel,1138032520 1717 | 474,2987,live action/animation,1138032544 1718 | 474,3000,anime,1138032418 1719 | 474,3006,tobacco,1138032361 1720 | 474,3006,true story,1138032361 1721 | 474,3007,In Netflix queue,1137201930 1722 | 474,3011,dance marathon,1137191967 1723 | 474,3028,Shakespeare,1137374544 1724 | 474,3030,samurai,1138032553 1725 | 474,3039,class,1138032532 1726 | 474,3040,camp,1137373789 1727 | 474,3044,memory,1137202357 1728 | 474,3044,music,1137202357 1729 | 474,3052,religion,1137191859 1730 | 474,3060,In Netflix queue,1137201892 1731 | 474,3071,high school,1137375023 1732 | 474,3072,New York,1137191274 1733 | 474,3077,In Netflix queue,1137201542 1734 | 474,3077,Up series,1137201543 1735 | 474,3081,Ichabod Crane,1137374535 1736 | 474,3087,christmas,1137271226 1737 | 474,3087,new york,1137271222 1738 | 474,3095,depression,1137205475 1739 | 474,3095,dust bowl,1137205475 1740 | 474,3097,remade,1138032435 1741 | 474,3098,baseball,1137201875 1742 | 474,3098,sports,1137201875 1743 | 474,3101,adultery,1138032312 1744 | 474,3108,homeless,1137374925 1745 | 474,3111,racism,1138032385 1746 | 474,3114,Pixar,1138032529 1747 | 474,3115,men in drag,1137191883 1748 | 474,3125,Graham Greene,1137191874 1749 | 474,3135,fatherhood,1138032346 1750 | 474,3147,Stephen King,1137191901 1751 | 474,3152,black-and-white,1137521245 1752 | 474,3160,L.A.,1137202527 1753 | 474,3174,Andy Kaufman,1137191943 1754 | 474,3175,spoof,1138032315 1755 | 474,3176,Europe,1137202659 1756 | 474,3176,murder,1137202659 1757 | 474,3178,boxing,1137374389 1758 | 474,3181,Shakespeare,1137179352 1759 | 474,3192,In Netflix queue,1137201464 1760 | 474,3210,high school,1137374381 1761 | 474,3211,a dingo ate my baby,1137191854 1762 | 474,3211,Australia,1137191854 1763 | 474,3247,nuns,1137374526 1764 | 474,3250,In Netflix queue,1137202163 1765 | 474,3258,plastic surgery,1137375095 1766 | 474,3259,Ireland,1137375116 1767 | 474,3260,E.M. Forster,1137181368 1768 | 474,3263,basketball,1137374566 1769 | 474,3270,figure skating,1137375084 1770 | 474,3273,slasher,1138032428 1771 | 474,3273,spoof,1138032428 1772 | 474,3281,sexuality,1137374330 1773 | 474,3306,big top,1137205399 1774 | 474,3306,circus,1137205399 1775 | 474,3307,blind,1137205412 1776 | 474,3310,orphans,1138032366 1777 | 474,3317,writing,1138032548 1778 | 474,3330,adolescence,1138032469 1779 | 474,3330,sexuality,1138032498 1780 | 474,3338,In Netflix queue,1137201680 1781 | 474,3341,remade,1138032286 1782 | 474,3360,basketball,1137784798 1783 | 474,3384,subway,1138032512 1784 | 474,3385,Peace Corp,1137374553 1785 | 474,3386,politics,1137191934 1786 | 474,3386,president,1137191934 1787 | 474,3408,scandal,1138032304 1788 | 474,3408,true story,1138032304 1789 | 474,3420,lawyers,1137375055 1790 | 474,3421,college,1137374581 1791 | 474,3421,TOGA,1137374587 1792 | 474,3424,racism,1137191865 1793 | 474,3429,aardman,1140626569 1794 | 474,3435,Insurance,1137520877 1795 | 474,3447,Pearl S Buck,1137191895 1796 | 474,3451,Hepburn and Tracy,1137181361 1797 | 474,3451,interracial marriage,1137205479 1798 | 474,3451,prejudice,1137205481 1799 | 474,3451,racism,1137205477 1800 | 474,3456,In Netflix queue,1137201463 1801 | 474,3461,island,1138032377 1802 | 474,3462,factory,1137205526 1803 | 474,3468,pool,1137521005 1804 | 474,3469,court,1137375171 1805 | 474,3469,evolution,1137375171 1806 | 474,3469,religion,1137375171 1807 | 474,3475,class,1138032413 1808 | 474,3481,Nick Hornby,1137181093 1809 | 474,3489,Peter Pan,1137191920 1810 | 474,3498,In Netflix queue,1137201900 1811 | 474,3502,death,1137375216 1812 | 474,3504,journalism,1137191950 1813 | 474,3507,In Netflix queue,1137200453 1814 | 474,3512,transplants,1137205572 1815 | 474,3515,alternate endings,1138032711 1816 | 474,3526,families,1138032724 1817 | 474,3528,psychiatrist,1137374751 1818 | 474,3536,priest,1137181376 1819 | 474,3536,rabbi,1137181376 1820 | 474,3546,show business,1138032886 1821 | 474,3548,1920s,1137205331 1822 | 474,3549,Gambling,1137191235 1823 | 474,3552,golf,1137374630 1824 | 474,3556,1970s,1138032871 1825 | 474,3556,suicide,1138032871 1826 | 474,3559,In Netflix queue,1137202015 1827 | 474,3566,business,1137205342 1828 | 474,3566,religion,1137205342 1829 | 474,3608,Pee Wee Herman,1138032732 1830 | 474,3629,mining,1138032660 1831 | 474,3675,Christmas,1137205636 1832 | 474,3676,In Netflix queue,1137202167 1833 | 474,3683,Coen Brothers,1137521124 1834 | 474,3712,television,1137375317 1835 | 474,3723,Shakespeare,1137191908 1836 | 474,3724,Vietnam,1137201954 1837 | 474,3730,spying,1138032598 1838 | 474,3735,police,1138032794 1839 | 474,3793,superhero,1137205670 1840 | 474,3801,court,1137191841 1841 | 474,3811,court,1138032584 1842 | 474,3816,missing children,1138032720 1843 | 474,3844,diabetes,1137375348 1844 | 474,3849,disability,1138032820 1845 | 474,3859,In Netflix queue,1137201554 1846 | 474,3859,televangelist,1137201554 1847 | 474,3877,superhero,1137374791 1848 | 474,3897,journalism,1137205322 1849 | 474,3897,music,1137205322 1850 | 474,3897,Rolling Stone,1137205322 1851 | 474,3910,blindness,1138032613 1852 | 474,3910,factory,1138032613 1853 | 474,3911,Dogs,1137181343 1854 | 474,3932,invisibility,1138032669 1855 | 474,3949,drug abuse,1137192093 1856 | 474,3951,In Netflix queue,1137179732 1857 | 474,3967,ballet,1137191989 1858 | 474,3983,family,1137205683 1859 | 474,3988,Dr. Seuss,1137373756 1860 | 474,3994,superhero,1137205619 1861 | 474,3996,china,1138032606 1862 | 474,3996,martial arts,1138032600 1863 | 474,4007,business,1138032878 1864 | 474,4012,stand-up comedy,1137374767 1865 | 474,4019,writing,1138032630 1866 | 474,4024,Edith Wharton,1137375144 1867 | 474,4025,pageant,1137374719 1868 | 474,4027,bluegrass,1137520959 1869 | 474,4029,movies,1137205589 1870 | 474,4034,based on a TV show,1137784729 1871 | 474,4046,Quakers,1138032635 1872 | 474,4077,psychopaths,1138039049 1873 | 474,4105,zombies,1137374672 1874 | 474,4113,Tennessee Williams,1137192025 1875 | 474,4117,In Netflix queue,1137200919 1876 | 474,4138,demons,1137373794 1877 | 474,4160,death penalty,1137205651 1878 | 474,4164,homeless,1138032589 1879 | 474,4164,New York,1138032589 1880 | 474,4166,reality TV,1137374777 1881 | 474,4171,Eugene O'Neill,1137192041 1882 | 474,4174,In Netflix queue,1137200882 1883 | 474,4184,Christmas,1138032577 1884 | 474,4189,Bible,1137375121 1885 | 474,4189,religion,1137375121 1886 | 474,4190,preacher,1137520919 1887 | 474,4190,religion,1137520919 1888 | 474,4191,sexuality,1138032573 1889 | 474,4194,In Netflix queue,1137201575 1890 | 474,4204,virginity,1137373770 1891 | 474,4210,Hannibal Lecter,1139059882 1892 | 474,4210,serial killer,1139059887 1893 | 474,4211,von Bulow,1137192103 1894 | 474,4214,college,1137374771 1895 | 474,4218,In Netflix queue,1137202093 1896 | 474,4221,football,1137374725 1897 | 474,4226,Backwards. memory,1137191263 1898 | 474,4232,spying,1138032824 1899 | 474,4246,singletons,1137205374 1900 | 474,4259,Nabokov,1137192047 1901 | 474,4263,alcoholism,1138032622 1902 | 474,4292,Union,1137192082 1903 | 474,4296,death,1137374704 1904 | 474,4306,fairy tales,1137205586 1905 | 474,4312,In Netflix queue,1137202173 1906 | 474,4316,figure skating,1137375157 1907 | 474,4326,race,1137375204 1908 | 474,4326,rasicm,1137375204 1909 | 474,4333,Strangers on a Train,1138032832 1910 | 474,4347,Holocaust,1138032626 1911 | 474,4356,In Netflix queue,1137201794 1912 | 474,4359,adultery,1138032800 1913 | 474,4361,men in drag,1138032838 1914 | 474,4370,robots,1137205306 1915 | 474,4380,accident,1138032770 1916 | 474,4396,race,1137373684 1917 | 474,4404,In Netflix queue,1137200537 1918 | 474,4447,lawyers,1138032674 1919 | 474,4448,heist,1138032773 1920 | 474,4465,rape,1138032565 1921 | 474,4476,twins,1137374622 1922 | 474,4477,circus,1137373669 1923 | 474,4489,Africa,1137766266 1924 | 474,4489,immigrants,1137374636 1925 | 474,4495,New York,1137202342 1926 | 474,4526,aliens,1137373948 1927 | 474,4537,hippies,1137205576 1928 | 474,4545,robots,1137374781 1929 | 474,4546,kidnapping,1138032855 1930 | 474,4546,remade,1138032855 1931 | 474,4558,twins,1137374798 1932 | 474,4571,time travel,1137205348 1933 | 474,4600,doctors,1137375124 1934 | 474,4621,babies,1137374699 1935 | 474,4623,baseball,1137374712 1936 | 474,4638,dinosaurs,1137374692 1937 | 474,4639,Hollywood,1137375071 1938 | 474,4639,movie business,1137375071 1939 | 474,4641,adolescence,1138032652 1940 | 474,4677,dogs,1137373991 1941 | 474,4678,television,1138032842 1942 | 474,4688,In Netflix queue,1137201126 1943 | 474,4787,prodigies,1138032687 1944 | 474,4801,family,1138032678 1945 | 474,4808,remake,1137374805 1946 | 474,4809,Nuclear disaster,1137367824 1947 | 474,4809,union,1137367824 1948 | 474,4812,NASA,1137374786 1949 | 474,4812,space,1137374786 1950 | 474,4823,wedding,1138032791 1951 | 474,4835,Loretta Lynn,1137192003 1952 | 474,4857,Judaism,1138039170 1953 | 474,4857,Tradition!,1137192017 1954 | 474,4874,aliens,1137375186 1955 | 474,4874,psychiatrist,1137375186 1956 | 474,4878,time travel,1137205781 1957 | 474,4881,Coen Brothers,1137192055 1958 | 474,4896,Magic,1137205893 1959 | 474,4896,Wizards,1137205893 1960 | 474,4902,ghosts,1137367852 1961 | 474,4920,psychology,1138039319 1962 | 474,4963,heist,1138039322 1963 | 474,4969,In Netflix queue,1137179563 1964 | 474,4970,nightclub,1138039117 1965 | 474,4970,singers,1138039117 1966 | 474,4973,whimsical,1137202289 1967 | 474,4979,new york,1137271220 1968 | 474,4980,time travel,1138039109 1969 | 474,4993,Tolkein,1137181125 1970 | 474,4998,racism,1139102582 1971 | 474,5008,Agatha Christie,1138039430 1972 | 474,5008,court,1138039430 1973 | 474,5012,cross dressing,1138039463 1974 | 474,5012,Judaism,1138039463 1975 | 474,5015,race,1138039271 1976 | 474,5034,Ghost,1137191298 1977 | 474,5064,Dumas,1138039163 1978 | 474,5064,revenge,1138039163 1979 | 474,5114,movie business,1138039083 1980 | 474,5120,In Netflix queue,1137200871 1981 | 474,5135,India,1137192060 1982 | 474,5139,baseball,1137373653 1983 | 474,5213,religion,1137373958 1984 | 474,5224,In Netflix queue,1137201464 1985 | 474,5238,In Netflix queue,1137202052 1986 | 474,5266,crime,1138039345 1987 | 474,5291,In Netflix queue,1137200907 1988 | 474,5291,samurai,1142282530 1989 | 474,5294,religion,1138039177 1990 | 474,5304,In Netflix queue,1137200961 1991 | 474,5316,World War II,1137375111 1992 | 474,5349,superhero,1137206068 1993 | 474,5353,blind,1137784621 1994 | 474,5377,Nick Hornby,1137181073 1995 | 474,5379,anti-Semitism,1137205732 1996 | 474,5379,Judaism,1137205732 1997 | 474,5398,boxing,1138039410 1998 | 474,5404,books,1138039075 1999 | 474,5445,future,1137206007 2000 | 474,5446,Australia,1137181573 2001 | 474,5450,sisterhood,1138039255 2002 | 474,5450,women,1138039255 2003 | 474,5451,disability,1137375287 2004 | 474,5451,mental illness,1137375287 2005 | 474,5470,Oscar Wilde,1137181530 2006 | 474,5475,In Netflix queue,1137201978 2007 | 474,5483,movie business,1137181538 2008 | 474,5498,In Netflix queue,1137179781 2009 | 474,5502,aliens,1137206065 2010 | 474,5505,adultery,1137205854 2011 | 474,5505,younger men,1137205854 2012 | 474,5527,AS Byatt,1138039372 2013 | 474,5527,books,1138039372 2014 | 474,5528,photography,1138039335 2015 | 474,5528,psychopaths,1138039335 2016 | 474,5548,homeless,1137374645 2017 | 474,5577,adolescence,1138039226 2018 | 474,5601,Animal movie,1138039452 2019 | 474,5603,crime,1138039241 2020 | 474,5603,heist,1138039241 2021 | 474,5618,anime,1137206076 2022 | 474,5629,Bible,1137374683 2023 | 474,5630,Hannibal Lecter,1139059884 2024 | 474,5633,bombs,1138039202 2025 | 474,5644,baseball,1138039381 2026 | 474,5644,Lou Gehrig,1138039381 2027 | 474,5669,violence in america,1138039133 2028 | 474,5673,pudding,1137206033 2029 | 474,5682,Holocaust,1138039189 2030 | 474,5685,Girl Power,1137206048 2031 | 474,5690,made me cry,1137205866 2032 | 474,5690,orphans,1137205866 2033 | 474,5721,In Netflix queue,1137200976 2034 | 474,5721,Judaism,1137200976 2035 | 474,5747,World War I,1137181512 2036 | 474,5791,art,1138039181 2037 | 474,5792,dating,1137375296 2038 | 474,5802,teenagers,1138039442 2039 | 474,5812,salute to Douglas Sirk,1137181505 2040 | 474,5816,Magic,1137205882 2041 | 474,5816,Wizards,1137205882 2042 | 474,5820,music business,1138039419 2043 | 474,5875,short films,1137375257 2044 | 474,5876,Graham Greene,1138039395 2045 | 474,5878,coma,1138039423 2046 | 474,5899,Africa,1137442538 2047 | 474,5952,Tolkein,1137181130 2048 | 474,5954,crime,1138039070 2049 | 474,5954,nightclub,1138039070 2050 | 474,5971,anime,1138039281 2051 | 474,5989,crime,1138039137 2052 | 474,5991,court,1138039145 2053 | 474,5991,jazz,1138039140 2054 | 474,5991,murder,1138039142 2055 | 474,5994,Dickens,1138039286 2056 | 474,5995,Holocaust,1137181562 2057 | 474,6002,drugs,1138307168 2058 | 474,6002,planes,1138307168 2059 | 474,6002,widows/widowers,1138307168 2060 | 474,6003,television,1138307058 2061 | 474,6016,crime,1138039157 2062 | 474,6016,photography,1138039157 2063 | 474,6016,violence,1138039157 2064 | 474,6020,class,1138307024 2065 | 474,6027,In Netflix queue,1137200848 2066 | 474,6031,motherhood,1138039234 2067 | 474,6031,race,1138039234 2068 | 474,6041,In Netflix queue,1137200888 2069 | 474,6063,doll,1137375194 2070 | 474,6090,ghosts,1138307131 2071 | 474,6101,Chile,1137900975 2072 | 474,6101,politics,1137900975 2073 | 474,6101,South America,1137900975 2074 | 474,6104,spoof,1138039279 2075 | 474,6156,England,1138307301 2076 | 474,6159,adolescence,1138307031 2077 | 474,6163,obsession,1137205905 2078 | 474,6170,horses,1138307048 2079 | 474,6178,blind,1138039358 2080 | 474,6178,blindness,1138039358 2081 | 474,6178,race,1138039358 2082 | 474,6181,Civil War,1138039404 2083 | 474,6181,Stephen Crane,1138039404 2084 | 474,6183,Day and Hudson,1138039366 2085 | 474,6183,sexuality,1138039366 2086 | 474,6195,books,1138498655 2087 | 474,6195,Stones of Summer,1138498655 2088 | 474,6197,mental illness,1138307351 2089 | 474,6201,England,1137191244 2090 | 474,6216,World War II,1138307229 2091 | 474,6218,soccer,1138039102 2092 | 474,6228,In Netflix queue,1137201613 2093 | 474,6232,animal movie,1138039126 2094 | 474,6232,lions,1138039125 2095 | 474,6235,Holocaust,1138307079 2096 | 474,6235,World War II,1138307079 2097 | 474,6238,immigrants,1138307096 2098 | 474,6242,television,1138307280 2099 | 474,6244,In Netflix queue,1137201921 2100 | 474,6245,prostitution,1138307361 2101 | 474,6254,screwball,1137181469 2102 | 474,6268,adolescence,1138307261 2103 | 474,6269,Big Brothers,1137206096 2104 | 474,6273,L.A.,1137521025 2105 | 474,6281,crime,1138307249 2106 | 474,6286,amnesia,1138039259 2107 | 474,6288,gangs,1138307044 2108 | 474,6289,Titanic,1138307085 2109 | 474,6291,prostitution,1138039248 2110 | 474,6296,mockumentary,1138039267 2111 | 474,6297,children,1138307101 2112 | 474,6299,birds,1138307384 2113 | 474,6305,Ray Bradbury,1137181490 2114 | 474,6308,lawyers,1137374834 2115 | 474,6327,1970s,1138486479 2116 | 474,6327,movies,1137200584 2117 | 474,6331,spelling bee,1138039807 2118 | 474,6333,Jean Grey,1137202706 2119 | 474,6333,Magneto,1137202706 2120 | 474,6333,Rogue,1137202706 2121 | 474,6333,superhero,1137205082 2122 | 474,6333,Wolverine,1137202706 2123 | 474,6334,teachers,1138039540 2124 | 474,6337,gambling,1138307244 2125 | 474,6339,trains,1138307176 2126 | 474,6341,art,1138307304 2127 | 474,6350,anime,1138039543 2128 | 474,6367,retro,1138039562 2129 | 474,6368,movies,1138307051 2130 | 474,6373,religion,1137375076 2131 | 474,6375,In Netflix queue,1137200486 2132 | 474,6375,They Might Be Giants,1137200486 2133 | 474,6377,Disney,1138039593 2134 | 474,6377,fish,1138039593 2135 | 474,6378,heist,1138307128 2136 | 474,6380,child abuse,1137205738 2137 | 474,6385,Girl Power,1137202810 2138 | 474,6390,Ninotchka remake,1138307320 2139 | 474,6400,crime,1138307197 2140 | 474,6407,Olympics,1137374882 2141 | 474,6407,Tokyo,1137374882 2142 | 474,6422,Civil War,1138039799 2143 | 474,6440,movie business,1138307037 2144 | 474,6452,In Netflix queue,1137201737 2145 | 474,6454,Nazis,1138307204 2146 | 474,6466,In Netflix queue,1137202027 2147 | 474,6502,zombies,1137181462 2148 | 474,6515,fugitive,1137206135 2149 | 474,6516,In Netflix queue,1137202127 2150 | 474,6535,politics,1137374839 2151 | 474,6536,swashbuckler,1138307326 2152 | 474,6539,swashbuckler,1138307257 2153 | 474,6545,Dodie Smith,1137181524 2154 | 474,6547,flood,1137375241 2155 | 474,6552,immigration,1138039559 2156 | 474,6565,horses,1138307288 2157 | 474,6579,Japan,1138307236 2158 | 474,6591,religion,1138039719 2159 | 474,6592,marriage,1138039795 2160 | 474,6603,Othello,1137375103 2161 | 474,6609,Bible,1138307089 2162 | 474,6620,cancer,1137205715 2163 | 474,6636,road trip,1138307356 2164 | 474,6639,blindness,1138307381 2165 | 474,6650,family,1138039708 2166 | 474,6650,multiple roles,1138039708 2167 | 474,6650,murder,1138039708 2168 | 474,6660,ballet,1138307265 2169 | 474,6662,Clousseau,1137375266 2170 | 474,6667,FBI,1138307378 2171 | 474,6668,In Netflix queue,1137179689 2172 | 474,6669,terminal illness,1138039639 2173 | 474,6708,con men,1138307183 2174 | 474,6708,fatherhood,1138307183 2175 | 474,6710,vertriloquism,1138307069 2176 | 474,6711,Japan,1138307143 2177 | 474,6713,In Netflix queue,1137201914 2178 | 474,6724,In Netflix queue,1137201684 2179 | 474,6732,matchmaker,1137375130 2180 | 474,6744,vampire,1137375236 2181 | 474,6753,Animal movie,1138307295 2182 | 474,6768,religion,1138039714 2183 | 474,6770,adultery,1138307212 2184 | 474,6770,terminal illness,1138307212 2185 | 474,6773,biking,1138307369 2186 | 474,6776,India,1137181544 2187 | 474,6785,Lonesome Polecat,1137206059 2188 | 474,6786,In Netflix queue,1137201845 2189 | 474,6787,journalism,1138039529 2190 | 474,6787,Watergate,1138039529 2191 | 474,6788,pregnancy,1137374818 2192 | 474,6791,Food,1137191312 2193 | 474,6807,british comedy,1138307193 2194 | 474,6810,domestic violence,1138307335 2195 | 474,6820,werewolf,1138039608 2196 | 474,6832,amnesia,1138307274 2197 | 474,6849,Christmas,1137374871 2198 | 474,6852,black and white,1138307125 2199 | 474,6852,Capote,1138307125 2200 | 474,6852,crime,1138307125 2201 | 474,6852,death penalty,1138307125 2202 | 474,6852,murder,1138307125 2203 | 474,6856,biopic,1138039917 2204 | 474,6863,school,1138307284 2205 | 474,6867,trains,1138039811 2206 | 474,6870,child abuse,1138307224 2207 | 474,6870,kidnapping,1138307224 2208 | 474,6874,martial arts,1138039672 2209 | 474,6874,revenge,1138039672 2210 | 474,6881,Thanksgiving,1137181567 2211 | 474,6890,adolescence,1138039574 2212 | 474,6890,violence in america,1138039574 2213 | 474,6909,transplants,1138039587 2214 | 474,6912,Rita Hayworth can dance!,1137179371 2215 | 474,6918,India,1138039870 2216 | 474,6932,journalism,1137181582 2217 | 474,6936,christmas,1138039581 2218 | 474,6944,remake,1137374824 2219 | 474,6944,wedding,1137374824 2220 | 474,6948,music business,1138039867 2221 | 474,6948,rap,1138039867 2222 | 474,6963,Amish,1137181478 2223 | 474,6970,Hepburn and Tracy,1137521576 2224 | 474,6979,Cold War,1138039874 2225 | 474,6981,religion,1138039909 2226 | 474,6984,Dickens,1138039822 2227 | 474,6985,saint,1137206024 2228 | 474,6993,family,1138039618 2229 | 474,7013,good and evil,1137206020 2230 | 474,7018,court,1137375276 2231 | 474,7018,Scott Turow,1137375276 2232 | 474,7023,In Netflix queue,1137179697 2233 | 474,7025,In Netflix queue,1137179593 2234 | 474,7049,Astaire and Rogers,1137784580 2235 | 474,7050,Astaire and Rogers,1137784592 2236 | 474,7053,Astaire and Rogers,1138039791 2237 | 474,7055,Astaire and Rogers,1138039819 2238 | 474,7059,horses,1138039728 2239 | 474,7061,terminal illness,1138039554 2240 | 474,7062,birds,1138039535 2241 | 474,7062,prison,1138039535 2242 | 474,7069,In Netflix queue,1137201628 2243 | 474,7069,Shakespeare,1137201628 2244 | 474,7070,cattle drive,1138039780 2245 | 474,7071,In Netflix queue,1137201154 2246 | 474,7073,Clousseau,1137375451 2247 | 474,7079,disability,1137375406 2248 | 474,7086,George Bernard Shaw,1137201703 2249 | 474,7087,E.M. Forster,1138039751 2250 | 474,7088,In Netflix queue,1137202031 2251 | 474,7090,martial arts,1138039622 2252 | 474,7091,Marx brothers,1138559500 2253 | 474,7121,Hepburn and Tracy,1137181204 2254 | 474,7132,Marx brothers,1138039735 2255 | 474,7132,opera,1138039735 2256 | 474,7139,immigrants,1138039653 2257 | 474,7141,In Netflix queue,1137201569 2258 | 474,7153,Tolkein,1137181217 2259 | 474,7156,Vietnam,1138039601 2260 | 474,7158,real estate,1137205933 2261 | 474,7160,serial killer,1138039725 2262 | 474,7162,Civil War,1138039548 2263 | 474,7190,Charlotte Bronte,1140047502 2264 | 474,7212,cross dressing,1138039664 2265 | 474,7212,men in drag,1138039664 2266 | 474,7217,amnesia,1137521621 2267 | 474,7218,In Netflix queue,1137179905 2268 | 474,7219,truckers,1138039826 2269 | 474,7234,circus,1137206103 2270 | 474,7256,mountain climbing,1138039857 2271 | 474,7285,adolescence,1138039837 2272 | 474,7301,priest,1137521429 2273 | 474,7301,religion,1137521429 2274 | 474,7318,religion,1138039757 2275 | 474,7323,Cold War,1138039614 2276 | 474,7361,memory,1137205808 2277 | 474,7371,Grace,1137205800 2278 | 474,7382,adolescence,1138039629 2279 | 474,7382,crime,1138039629 2280 | 474,7438,revenge,1137205948 2281 | 474,7440,Holocaust,1142279175 2282 | 474,7451,High School,1137205962 2283 | 474,7479,World War II,1138039893 2284 | 474,7493,multiple personalities,1138039850 2285 | 474,7572,cancer,1137181595 2286 | 474,7584,Hepburn and Tracy,1138039906 2287 | 474,7584,marriage,1138039906 2288 | 474,7614,Rogers and Hammerstein,1138040199 2289 | 474,7618,biopic,1137201647 2290 | 474,7618,In Netflix queue,1137201648 2291 | 474,7619,blindness,1138040167 2292 | 474,7619,deaf,1138040171 2293 | 474,7646,Stephen King,1137374860 2294 | 474,7649,space station,1138039958 2295 | 474,7700,In Netflix queue,1137201116 2296 | 474,7705,Hepburn and Tracy,1137521641 2297 | 474,7705,sports,1137521641 2298 | 474,7713,sexuality,1138040011 2299 | 474,7714,King Arthur,1138039998 2300 | 474,7728,adultery,1138040205 2301 | 474,7748,In Netflix queue,1137202002 2302 | 474,7766,MacBeth,1138040268 2303 | 474,7789,terrorism,1138039932 2304 | 474,7792,In Netflix queue,1137200939 2305 | 474,7831,Nick and Nora Charles,1137201638 2306 | 474,7832,Nick and Nora Charles,1137201717 2307 | 474,7833,Nick and Nora Charles,1137201661 2308 | 474,7834,Nick and Nora Charles,1137200666 2309 | 474,7835,Nick and Nora Charles,1137201747 2310 | 474,7924,In Netflix queue,1137201677 2311 | 474,7926,In Netflix queue,1137179753 2312 | 474,7932,Amtrak,1137271192 2313 | 474,7932,Homeless,1137271192 2314 | 474,7932,independent,1137271192 2315 | 474,7932,New York,1137271192 2316 | 474,7932,Sundance award winner,1137271191 2317 | 474,7932,Train,1137271192 2318 | 474,7934,psychology,1138040275 2319 | 474,7981,police,1138040118 2320 | 474,7983,show business,1138039989 2321 | 474,8011,politics,1143998541 2322 | 474,8014,In Netflix queue,1137200544 2323 | 474,8019,remade,1138040062 2324 | 474,8033,In Netflix queue,1137200492 2325 | 474,8094,In Netflix queue,1137179631 2326 | 474,8153,Van Gogh,1137375419 2327 | 474,8167,swashbuckler,1138040004 2328 | 474,8183,In Netflix queue,1137201001 2329 | 474,8188,Not available from Netflix,1137179956 2330 | 474,8190,In Netflix queue,1137201864 2331 | 474,8191,Anne Boleyn,1138039949 2332 | 474,8197,In Netflix queue,1137202144 2333 | 474,8207,assassination,1138040067 2334 | 474,8228,heist,1138040147 2335 | 474,8235,Clock,1137202793 2336 | 474,8266,Prince,1137375433 2337 | 474,8275,In Netflix queue,1137201789 2338 | 474,8337,military,1138039993 2339 | 474,8338,convent,1138039973 2340 | 474,8338,religion,1138039973 2341 | 474,8341,Dickens,1137200651 2342 | 474,8360,ogres,1138040236 2343 | 474,8366,pregnancy,1138040220 2344 | 474,8366,religion,1138040220 2345 | 474,8368,Magic,1137202784 2346 | 474,8368,Wizards,1137202784 2347 | 474,8375,politics,1137375412 2348 | 474,8376,high school,1138040176 2349 | 474,8379,In Netflix queue,1137200899 2350 | 474,8382,death,1137375392 2351 | 474,8424,divorce,1138040077 2352 | 474,8446,World War II,1137375442 2353 | 474,8463,deafness,1138040133 2354 | 474,8464,food,1138040254 2355 | 474,8464,McDonalds,1138040254 2356 | 474,8507,circus,1138040090 2357 | 474,8530,Ireland,1138040071 2358 | 474,8580,fairy tales,1137201021 2359 | 474,8580,In Netflix queue,1137201021 2360 | 474,8607,In Netflix queue,1137201077 2361 | 474,8609,In Netflix queue,1137179727 2362 | 474,8622,politics,1138040083 2363 | 474,8622,terrorism,1138040083 2364 | 474,8636,Doc Ock,1137202798 2365 | 474,8638,generation X,1138039967 2366 | 474,8645,drugs,1138040155 2367 | 474,8645,immigrants,1138040155 2368 | 474,8695,court,1137375369 2369 | 474,8714,Cole Porter,1137375425 2370 | 474,8754,Oscar (Best Actress),1142282521 2371 | 474,8765,In Netflix queue,1137179707 2372 | 474,8784,psychology,1138040105 2373 | 474,8796,Rome,1137375385 2374 | 474,8809,Australia,1138040052 2375 | 474,8827,Stand Up,1138039969 2376 | 474,8838,In Netflix queue,1137179758 2377 | 474,8874,zombies,1138040225 2378 | 474,8875,alcoholism,1138040042 2379 | 474,8875,marriage,1138040042 2380 | 474,8879,Agatha Christie,1145755135 2381 | 474,8879,Oscar (Best Supporting Actress),1145755138 2382 | 474,8879,train,1145755140 2383 | 474,8880,disability,1138040162 2384 | 474,8914,time travel,1138040211 2385 | 474,8920,Oscar (Best Actress),1142282522 2386 | 474,8938,parenthood,1138040262 2387 | 474,8938,psychology,1138040262 2388 | 474,8943,stage,1137375373 2389 | 474,8949,wine,1138040242 2390 | 474,8950,psychology,1138040138 2391 | 474,8951,abortion,1138040272 2392 | 474,8958,biopic,1138040214 2393 | 474,8961,Disney,1138040115 2394 | 474,8961,superhero,1138040115 2395 | 474,8970,Peter Pan,1138040086 2396 | 474,8979,Hearst,1137521449 2397 | 474,8983,martial arts,1138040109 2398 | 474,8998,In Netflix queue,1137179738 2399 | 474,9018,journalism,1138040046 2400 | 474,25753,gold,1139940948 2401 | 474,25825,revenge,1137205843 2402 | 474,25841,show business,1138040249 2403 | 474,25855,In Netflix queue,1137179637 2404 | 474,25865,In Netflix queue,1137201652 2405 | 474,25886,made me cry,1137206038 2406 | 474,25940,mirrors,1137521290 2407 | 474,25952,In Netflix queue,1137179713 2408 | 474,26085,Not available from Netflix,1137179665 2409 | 474,26131,In Netflix queue,1137200982 2410 | 474,26151,donkey,1145749895 2411 | 474,26242,In Netflix queue,1137179570 2412 | 474,26662,anime,1138040136 2413 | 474,26776,In Netflix queue,1137179786 2414 | 474,27731,In Netflix queue,1137201122 2415 | 474,27741,In Netflix queue,1137179587 2416 | 474,27790,religion,1138040326 2417 | 474,27790,saints,1138040326 2418 | 474,27820,Animal movie,1138040358 2419 | 474,27820,camels,1138040358 2420 | 474,27834,fatherhood,1138040348 2421 | 474,27838,revenge,1137205953 2422 | 474,27878,India,1138038974 2423 | 474,27882,surfing,1139939407 2424 | 474,30707,boxing,1138040320 2425 | 474,30749,genocide,1137205917 2426 | 474,30793,roald dahl,1138038979 2427 | 474,30812,biopic,1138038949 2428 | 474,30812,Howard Hughes,1138038949 2429 | 474,30812,movie business,1138038949 2430 | 474,30820,child abuse,1137206121 2431 | 474,30822,busniess,1137205938 2432 | 474,30850,Shakespeare,1137181555 2433 | 474,31030,motherhood,1138040311 2434 | 474,31437,orphans,1138040341 2435 | 474,31658,06 Oscar Nominated Best Movie - Animation,1145451856 2436 | 474,31658,In Netflix queue,1137201531 2437 | 474,31903,marriage,1138040364 2438 | 474,31923,In Netflix queue,1137200865 2439 | 474,32160,In Netflix queue,1137179674 2440 | 474,32371,In Netflix queue,1137202044 2441 | 474,32582,birds,1138804206 2442 | 474,32582,parrots,1138804206 2443 | 474,32584,fatherhood,1138038959 2444 | 474,33154,business,1137766122 2445 | 474,33154,money,1137766122 2446 | 474,33154,scandal,1137766123 2447 | 474,33166,racism,1138038982 2448 | 474,33493,space,1137206087 2449 | 474,33493,space opera,1137206087 2450 | 474,33660,boxing,1137201602 2451 | 474,34271,hip hop,1141848751 2452 | 474,34359,pregnancy,1138040301 2453 | 474,34405,Firefly,1137181624 2454 | 474,34437,In Netflix queue,1137179720 2455 | 474,34482,In Netflix queue,1137202180 2456 | 474,34528,pregnancy,1139693892 2457 | 474,34542,Animal movie,1138039000 2458 | 474,34542,bears,1138039000 2459 | 474,35015,Animal movie,1137180081 2460 | 474,35015,In Netflix queue,1137202200 2461 | 474,36517,Africa,1139234947 2462 | 474,36517,business,1139234947 2463 | 474,36517,politics,1139234947 2464 | 474,36527,mathematics,1137201998 2465 | 474,36535,In Netflix queue,1137201162 2466 | 474,37240,In Netflix queue,1138804230 2467 | 474,37729,06 Oscar Nominated Best Movie - Animation,1139059938 2468 | 474,37733,In Netflix queue,1137200955 2469 | 474,37736,Dickens,1139940656 2470 | 474,37741,Truman Capote,1142996187 2471 | 474,38038,06 Oscar Nominated Best Movie - Animation,1139664190 2472 | 474,38038,Aardman,1139664221 2473 | 474,38061,In Netflix queue,1137179624 2474 | 474,38886,divorce,1145451837 2475 | 474,39234,courtroom drama,1140576326 2476 | 474,39234,In Netflix queue,1137202089 2477 | 474,39292,In Netflix queue,1137201595 2478 | 474,39292,journalism,1137201595 2479 | 474,39292,McCarthy hearings,1137201595 2480 | 474,39292,Morrow,1137201595 2481 | 474,40583,In Netflix queue,1137200527 2482 | 474,40629,In Netflix queue,1137179797 2483 | 474,40815,Magic,1137202776 2484 | 474,40815,Wizards,1137202776 2485 | 474,40819,Johnny Cash,1137200595 2486 | 474,41566,C.S. Lewis,1137181617 2487 | 474,41997,In Netflix queue,1137179603 2488 | 474,42002,In Netflix queue,1137202150 2489 | 474,42740,In Netflix queue,1138804237 2490 | 477,32,Brad Pitt,1242494310 2491 | 477,32,Bruce Willis,1242494306 2492 | 477,32,mindfuck,1242494321 2493 | 477,32,Post apocalyptic,1242494300 2494 | 477,32,post-apocalyptic,1242494300 2495 | 477,32,remake,1242494315 2496 | 477,32,time travel,1242494304 2497 | 477,32,twist ending,1242494302 2498 | 477,34,villain nonexistent or not needed for good story,1244787845 2499 | 477,104,Adam Sandler,1244787691 2500 | 477,216,Adam Sandler,1244787718 2501 | 477,216,stop looking at me swan,1244787726 2502 | 477,318,Morgan Freeman,1244566076 2503 | 477,541,androids,1262795764 2504 | 477,541,artificial intelligence,1262795769 2505 | 477,541,atmospheric,1262795768 2506 | 477,541,cyberpunk,1262795770 2507 | 477,541,future,1262795772 2508 | 477,541,mindfuck,1262795778 2509 | 477,541,Philip K. Dick,1262795776 2510 | 477,589,Scifi masterpiece,1201166474 2511 | 477,593,disturbing,1241396359 2512 | 477,593,drama,1241396391 2513 | 477,593,gothic,1241396378 2514 | 477,593,psychology,1241396367 2515 | 477,593,suspense,1241396389 2516 | 477,733,Michael Bay,1241938858 2517 | 477,733,terrorism,1241938853 2518 | 477,750,black comedy,1242494959 2519 | 477,750,dark comedy,1242494953 2520 | 477,750,satire,1242494951 2521 | 477,832,GIVE ME BACK MY SON!,1269832636 2522 | 477,832,It was melodramatic and kind of dumb,1269832633 2523 | 477,832,Mel Gibson,1269832629 2524 | 477,903,Alfred Hitchcock,1242577125 2525 | 477,903,Atmospheric,1242577135 2526 | 477,903,imdb top 250,1242577117 2527 | 477,903,James Stewart,1242577112 2528 | 477,904,imdb top 250,1242577077 2529 | 477,904,James Stewart,1242577056 2530 | 477,904,mystery,1242577064 2531 | 477,904,photographer,1242577068 2532 | 477,904,photography,1242577070 2533 | 477,908,Alfred Hitchcock,1242577097 2534 | 477,908,imdb top 250,1242577100 2535 | 477,922,eerie,1245030098 2536 | 477,922,movie business,1245030089 2537 | 477,1089,humorous,1242494883 2538 | 477,1089,neo-noir,1242494879 2539 | 477,1089,Quentin Tarantino,1242494874 2540 | 477,1089,religion,1242494896 2541 | 477,1089,Tarantino,1242494887 2542 | 477,1196,classic,1262795806 2543 | 477,1196,George Lucas,1262795793 2544 | 477,1196,Harrison Ford,1262795792 2545 | 477,1196,music,1262795815 2546 | 477,1196,original plot,1262795808 2547 | 477,1196,sci-fi,1262795797 2548 | 477,1196,sequel,1262795800 2549 | 477,1219,Alfred Hitchcock,1242577153 2550 | 477,1219,black and white,1242577173 2551 | 477,1219,imdb top 250,1242577161 2552 | 477,1219,remade,1242577165 2553 | 477,1222,anti-war,1242494925 2554 | 477,1274,animation,1244787931 2555 | 477,1274,anime,1244787929 2556 | 477,1274,visually stunning,1244787944 2557 | 477,2393,cameo:Whoopi Goldberg,1242278473 2558 | 477,2393,space opera,1242278463 2559 | 477,2393,Star Trek,1242278464 2560 | 477,2694,Adam Sandler,1244787788 2561 | 477,2706,best comedy,1244707216 2562 | 477,2706,Chris Klein,1244707221 2563 | 477,2706,dumb,1244707211 2564 | 477,2706,Jason Biggs,1244707223 2565 | 477,2706,not funny,1244707227 2566 | 477,2706,pizza beer,1244707207 2567 | 477,2706,Seann William Scott,1244707224 2568 | 477,2706,teen,1244707202 2569 | 477,2761,animation,1244787872 2570 | 477,3000,adventure,1241396438 2571 | 477,3000,atmospheric,1241396434 2572 | 477,3000,fantasy world,1241396427 2573 | 477,3000,Studio Ghibli,1241396420 2574 | 477,3000,surreal,1241396422 2575 | 477,3527,aliens,1278683918 2576 | 477,3527,Arnold Schwarzenegger,1269832592 2577 | 477,3527,dialogue,1269832605 2578 | 477,3527,guns,1278683912 2579 | 477,3527,Jesse Ventura,1269832593 2580 | 477,3527,macho,1269832597 2581 | 477,3527,sci-fi,1269832609 2582 | 477,3527,scifi cult,1269832612 2583 | 477,3994,atmospheric,1241396301 2584 | 477,3994,comics,1241396280 2585 | 477,3994,father-son relationship,1241396313 2586 | 477,3994,mindfuck,1241396308 2587 | 477,3994,unique,1241396293 2588 | 477,4226,mystery,1256051072 2589 | 477,4226,twist ending,1256051079 2590 | 477,4454,claymation,1245030193 2591 | 477,4454,creativity,1245030187 2592 | 477,4454,dystopia,1245030190 2593 | 477,4454,free to download,1245030175 2594 | 477,4454,imagination,1245030178 2595 | 477,4454,no dialogue,1245030196 2596 | 477,4454,social commentary,1245030185 2597 | 477,4725,mental hospital,1252820163 2598 | 477,4725,psychology,1252820170 2599 | 477,4725,suspense,1252820168 2600 | 477,4725,Well Done,1252820180 2601 | 477,4878,dreamlike,1242494462 2602 | 477,4878,hallucinatory,1242494486 2603 | 477,4878,mental illness,1242494483 2604 | 477,4878,psychology,1242494469 2605 | 477,4878,social commentary,1242494478 2606 | 477,4878,teen,1242494496 2607 | 477,4878,thought-provoking,1242494462 2608 | 477,5459,crappy sequel,1262795620 2609 | 477,5459,first was much better,1262795632 2610 | 477,5459,sequel,1262795653 2611 | 477,7361,cult film,1242494340 2612 | 477,7361,imagination,1256051047 2613 | 477,7361,Jim Carrey,1242494357 2614 | 477,7361,love,1256051045 2615 | 477,7361,surreal,1256051041 2616 | 477,7439,comic book,1201196824 2617 | 477,8783,Atmospheric,1242580424 2618 | 477,8783,good soundtrack,1242580444 2619 | 477,8783,M. Night Shyamalan,1242580441 2620 | 477,8961,animation,1244789192 2621 | 477,8961,Samuel L. Jackson,1244789197 2622 | 477,41569,big budget,1262795959 2623 | 477,42422,love,1252377761 2624 | 477,45499,Halle Berry,1262795979 2625 | 477,45499,Hugh Jackman,1262795972 2626 | 477,45730,bad,1242580531 2627 | 477,45730,disappointing,1242580521 2628 | 477,45730,faerie tale,1242580526 2629 | 477,45730,fairy tale,1242580523 2630 | 477,45730,far fetched,1242580497 2631 | 477,45730,M. Night Shyamalan,1242580504 2632 | 477,45730,Paul Giamatti,1242580508 2633 | 477,47610,costume drama,1242580608 2634 | 477,47610,Edward Norton,1242580578 2635 | 477,47610,magic,1242580595 2636 | 477,47610,Paul Giamatti,1242580580 2637 | 477,52885,dreamlike,1256051008 2638 | 477,53468,zombies,1254101434 2639 | 477,54503,friendship,1244566102 2640 | 477,54503,high school,1244566117 2641 | 477,54503,Michael Cera,1244566094 2642 | 477,54503,nerds,1244566133 2643 | 477,54503,self discovery,1244566119 2644 | 477,54503,shenanigans,1244566122 2645 | 477,54503,virginity,1244566127 2646 | 477,55167,anime,1282924582 2647 | 477,56174,alone in the world,1262795676 2648 | 477,56174,apocalypse,1262795677 2649 | 477,56174,based on a book,1262795681 2650 | 477,56174,last man on earth,1262795684 2651 | 477,56174,plot holes,1262795671 2652 | 477,56174,Post apocalyptic,1262795695 2653 | 477,56174,post-apocalyptic,1262795699 2654 | 477,56174,religion,1262795669 2655 | 477,56174,vampire,1262795702 2656 | 477,56367,Michael Cera,1244566158 2657 | 477,56367,notable soundtrack,1244566156 2658 | 477,56367,pregnancy,1244566154 2659 | 477,56367,teenage pregnancy,1244566153 2660 | 477,56367,witty,1244566176 2661 | 477,57669,black comedy,1269832562 2662 | 477,57669,Colin Farrell,1269832561 2663 | 477,57669,dark comedy,1269832559 2664 | 477,57669,drugs,1269832564 2665 | 477,57669,friendship,1269832567 2666 | 477,57669,irreverent,1269832579 2667 | 477,57669,Ralph Fiennes,1269832572 2668 | 477,57669,stylized,1269832574 2669 | 477,59141,movies about movies,1241396155 2670 | 477,60069,last man on earth,1241396252 2671 | 477,60069,love story,1241396256 2672 | 477,60069,Post apocalyptic,1241396239 2673 | 477,60069,social commentary,1241396236 2674 | 477,61323,Brad Pitt,1269832483 2675 | 477,61323,Coen Bros,1269832486 2676 | 477,61323,Coen Brothers,1269832484 2677 | 477,61323,dark comedy,1269832488 2678 | 477,61323,espionage,1269832491 2679 | 477,61323,George Clooney,1269832490 2680 | 477,61323,John Malkovich,1269832492 2681 | 477,61323,satire,1269832497 2682 | 477,61323,twists & turns,1269832504 2683 | 477,61323,weird,1269832501 2684 | 477,62336,2D animation,1282924611 2685 | 477,62336,cult film,1282924621 2686 | 477,62336,stylish,1282924615 2687 | 477,62336,surreal,1282924617 2688 | 477,66934,joss whedon,1244788368 2689 | 477,66934,Nathan Fillion,1244788378 2690 | 477,66934,Neil Patrick Harris,1244788374 2691 | 477,66934,parody,1244788359 2692 | 477,67087,Andy Samberg,1269833868 2693 | 477,67087,bromance,1269833887 2694 | 477,67087,bromantic,1269833884 2695 | 477,67087,comedy,1269833878 2696 | 477,67087,Jaime Pressly,1269833873 2697 | 477,67087,Jason Segel,1269833863 2698 | 477,67087,masculinity,1269833871 2699 | 477,67087,Paul Rudd,1269833866 2700 | 477,68237,2001-like,1252898614 2701 | 477,68237,Sci-fi,1252898620 2702 | 477,68237,solitude,1252898625 2703 | 477,68319,bad plot,1262795886 2704 | 477,68319,comic book,1241938912 2705 | 477,68319,hugh jackman,1262795892 2706 | 477,68319,Ryan Reynolds,1262795891 2707 | 477,68319,too many characters,1241941484 2708 | 477,68358,future,1245030304 2709 | 477,68358,lack of development,1262795924 2710 | 477,68358,lack of story,1262795926 2711 | 477,68358,quick cuts,1245030309 2712 | 477,68358,sci-fi,1262795933 2713 | 477,68358,Simon Pegg,1245030316 2714 | 477,68358,space,1262795932 2715 | 477,68358,space travel,1262795931 2716 | 477,68358,time travel,1262795929 2717 | 477,68791,artificial intelligence,1262795849 2718 | 477,68791,bad acting,1262795862 2719 | 477,68791,bad plot,1262795842 2720 | 477,68791,bad script,1262795859 2721 | 477,68791,Christian Bale,1262795846 2722 | 477,68791,FIGHTING THE SYSTEM,1262795852 2723 | 477,68791,franchise,1262795875 2724 | 477,68791,new composer,1243401981 2725 | 477,68791,post-apocalyptic,1262795854 2726 | 477,68791,robots,1243401992 2727 | 477,68791,sci-fi,1262795871 2728 | 477,68791,sequel,1262795873 2729 | 477,68945,Recap,1246349071 2730 | 477,69526,bad plot,1262795720 2731 | 477,69526,Michael Bay,1262795729 2732 | 477,69526,needed more autobots,1246346162 2733 | 477,69526,plot holes,1262795732 2734 | 477,69526,ridiculous,1262795737 2735 | 477,69526,Shia LaBeouf,1246346172 2736 | 477,69526,stop using useless characters for filler,1246346159 2737 | 477,69757,artistic,1279956134 2738 | 477,69757,Funny,1279956141 2739 | 477,69757,humorous,1279956124 2740 | 477,69757,inspiring,1279956130 2741 | 477,69757,intelligent,1279956132 2742 | 477,69757,quirky,1279956145 2743 | 477,69757,romance,1279956123 2744 | 477,69757,Zooey Deschanel,1279956120 2745 | 477,70286,intelligent sci-fi,1250476969 2746 | 477,72224,bad humor,1296891269 2747 | 477,72998,bad science,1262707524 2748 | 477,72998,futuristic,1262707531 2749 | 477,72998,graphic design,1262707532 2750 | 477,72998,James Cameron,1262707536 2751 | 477,72998,military,1262707534 2752 | 477,72998,poor dialogue,1262707537 2753 | 477,72998,sci-fi,1262707551 2754 | 477,72998,superficial plot,1262707525 2755 | 477,72998,unoriginal,1262794416 2756 | 477,72998,white guilt,1262707542 2757 | 477,79132,dreamlike,1280608188 2758 | 477,79132,surreal,1280608178 2759 | 477,79132,visually appealing,1280608181 2760 | 477,79702,geeky,1282923864 2761 | 477,79702,Michael Cera,1282923853 2762 | 477,79702,stylized,1282923856 2763 | 477,79702,video games,1282923860 2764 | 477,82242,male nudity,1293690470 2765 | 477,82461,action choreography,1296891206 2766 | 477,82461,Horrid characterisation,1296891200 2767 | 477,82461,Poor plot development,1296891195 2768 | 477,82461,sequel,1296891202 2769 | 477,82461,soundtrack,1296891203 2770 | 487,112552,passion,1429347541 2771 | 487,112552,psychological,1429347541 2772 | 487,112552,suspense,1429347541 2773 | 506,112552,jazz,1424487178 2774 | 506,112552,music,1424487178 2775 | 506,112552,tense,1424487178 2776 | 509,80834,adventure,1435992979 2777 | 509,80834,animation,1435992979 2778 | 509,80834,fantasy,1435992979 2779 | 513,750,purity of essence,1159980456 2780 | 513,750,Slim Pickens,1159980456 2781 | 513,6787,Deep Throat,1159980769 2782 | 520,3039,Dan Aykroyd,1326608424 2783 | 520,60516,enjoyable,1326610782 2784 | 533,356,bubba gump shrimp,1424753866 2785 | 533,356,lieutenant dan,1424753866 2786 | 533,356,stupid is as stupid does,1424753866 2787 | 537,527,based on a true story,1424142072 2788 | 537,527,biography,1424142112 2789 | 537,527,disturbing,1424142113 2790 | 537,527,holocaust,1424142110 2791 | 537,1625,Mystery,1424141701 2792 | 537,1721,romance,1424141922 2793 | 537,2329,edward norton,1424141449 2794 | 537,2329,emotional,1424141459 2795 | 537,2329,politics,1424141461 2796 | 537,2571,philosophy,1424141098 2797 | 537,2571,post apocalyptic,1424141101 2798 | 537,3949,depressing,1424141547 2799 | 537,3949,psychology,1424141551 2800 | 537,5989,cheating,1424139697 2801 | 537,5989,intelligent,1424139627 2802 | 537,5989,Leonardo DiCaprio,1424139605 2803 | 537,5989,lies,1424139629 2804 | 537,5989,smart,1424139670 2805 | 537,5989,Tom Hanks,1424139608 2806 | 537,5989,twists & turns,1424139659 2807 | 537,8533,memory loss,1424141963 2808 | 537,8533,romance,1424141953 2809 | 537,8533,sad,1424141974 2810 | 537,55765,corruption,1424139519 2811 | 537,55765,Denzel Washington,1424139515 2812 | 537,55765,mafia,1424139559 2813 | 537,55765,organized crime,1424139529 2814 | 537,58295,heist,1424139043 2815 | 537,58295,robbery,1424139058 2816 | 537,58295,small time criminals,1424139056 2817 | 537,69122,casino,1424140307 2818 | 537,69122,comedy,1424140299 2819 | 537,69122,funny,1424140317 2820 | 537,69122,hotel,1424140308 2821 | 537,69122,Las Vegas,1424140298 2822 | 537,69481,war,1424144802 2823 | 537,72998,beautiful scenery,1424140229 2824 | 537,72998,graphic design,1424140242 2825 | 537,72998,music,1424140259 2826 | 537,72998,politics,1424140263 2827 | 537,72998,romance,1424140252 2828 | 537,72998,thought-provoking,1424140286 2829 | 537,72998,too long,1424140237 2830 | 537,72998,visually stunning,1424140233 2831 | 537,79132,big budget,1424140151 2832 | 537,79132,clever,1424140142 2833 | 537,79132,complicated,1424140133 2834 | 537,79132,dead wife,1424140200 2835 | 537,79132,great soundtrack,1424140118 2836 | 537,79132,heist,1424140154 2837 | 537,79132,intellectual,1424140159 2838 | 537,79132,mindfuck,1424140185 2839 | 537,79132,philosophy,1424140124 2840 | 537,79132,psychological,1424140165 2841 | 537,79132,psychology,1424140162 2842 | 537,79132,suspense,1424140144 2843 | 537,79132,thought-provoking,1424140130 2844 | 537,80489,crime,1424139483 2845 | 537,80489,FBI,1424139489 2846 | 537,80489,romance,1424139421 2847 | 537,80489,too much love interest,1424139454 2848 | 537,80906,business,1424461859 2849 | 537,80906,corruption,1424461848 2850 | 537,80906,economics,1424461852 2851 | 537,80906,investor corruption,1424461854 2852 | 537,80906,politics,1424461849 2853 | 537,80906,truth,1424461857 2854 | 537,89745,Captain America,1424140576 2855 | 537,89745,silly,1424140613 2856 | 537,89745,superhero,1424140571 2857 | 537,89745,superhero team,1424140598 2858 | 537,90439,bank,1424141059 2859 | 537,90439,big corporations,1424138651 2860 | 537,90439,business,1424138635 2861 | 537,90439,corruption,1424138654 2862 | 537,90439,financial crisis,1424138667 2863 | 537,90439,Kevin Spacey,1424138645 2864 | 537,90439,realistic,1424138641 2865 | 537,90439,suspense,1424141005 2866 | 537,90439,Wall Street,1424141052 2867 | 537,92259,emotional,1424141413 2868 | 537,92259,sexuality,1424141386 2869 | 537,95067,interesting scenario,1424140932 2870 | 537,95441,crude humor,1424140358 2871 | 537,97304,cia,1424138568 2872 | 537,97304,politics,1424138581 2873 | 537,97304,Thrilling,1424138560 2874 | 537,99114,Soundtrack,1424140415 2875 | 537,104218,Adam Sandler,1424317302 2876 | 537,104841,cliche characters,1424140813 2877 | 537,104841,long shots,1424140806 2878 | 537,104841,predictable,1424140784 2879 | 537,104841,Simple,1424140794 2880 | 537,104841,slow action,1424140827 2881 | 537,104841,visually appealing,1424140838 2882 | 537,105504,hostage,1424141178 2883 | 537,105504,ocean,1424141170 2884 | 537,105504,suspense,1424141149 2885 | 537,106782,Stock Market,1424142693 2886 | 537,106782,Wall Street,1424142688 2887 | 543,85565,Comedy,1377022063 2888 | 567,1,fun,1525286013 2889 | 567,101,off-beat comedy,1525287216 2890 | 567,101,quirky,1525287214 2891 | 567,308,cynical,1525287798 2892 | 567,356,bittersweet,1525287545 2893 | 567,356,emotional,1525287537 2894 | 567,356,heartwarming,1525287541 2895 | 567,356,touching,1525287539 2896 | 567,541,atmospheric,1525286590 2897 | 567,541,dreamlike,1525286588 2898 | 567,541,existentialism,1525286583 2899 | 567,541,philosophical,1525286586 2900 | 567,750,black comedy,1525287727 2901 | 567,750,dark comedy,1525287724 2902 | 567,750,Quirky,1525287729 2903 | 567,1203,claustrophobic,1525283652 2904 | 567,1203,confrontational,1525283610 2905 | 567,1203,earnest,1525283658 2906 | 567,1203,good dialogue,1525283605 2907 | 567,1203,great screenplay,1525283612 2908 | 567,1203,gritty,1525283636 2909 | 567,1203,Motivational,1525283631 2910 | 567,1203,thought-provoking,1525283607 2911 | 567,1237,atmospheric,1525286526 2912 | 567,1237,cerebral,1525286536 2913 | 567,1237,existentialism,1525286523 2914 | 567,1237,philosophical,1525286544 2915 | 567,1237,reflective,1525286538 2916 | 567,1260,atmospheric,1525285649 2917 | 567,1260,chilly,1525285661 2918 | 567,1260,creepy,1525285667 2919 | 567,1260,menacing,1525285665 2920 | 567,1260,mental illness,1525285654 2921 | 567,1260,oninous,1525285670 2922 | 567,1653,intelligent,1525287116 2923 | 567,1653,thought-provoking,1525287118 2924 | 567,1704,heartwarming,1525287326 2925 | 567,1704,inspirational,1525287323 2926 | 567,1916,avant-garde romantic comedy,1525282933 2927 | 567,1916,elegiac,1525282961 2928 | 567,1916,melancholy,1525282935 2929 | 567,1916,quirky,1525282930 2930 | 567,1916,wry,1525282972 2931 | 567,1921,artsy,1525287302 2932 | 567,1921,atmospheric,1525287273 2933 | 567,1921,cerebral,1525287286 2934 | 567,1921,enigmatic,1525287289 2935 | 567,1921,existentialism,1525287275 2936 | 567,1921,hallucinatory,1525287271 2937 | 567,1921,insanity,1525287293 2938 | 567,1921,paranoia,1525287295 2939 | 567,1921,paranoid,1525287277 2940 | 567,1921,surreal,1525287281 2941 | 567,1921,tense,1525287285 2942 | 567,1921,visually appealing,1525287283 2943 | 567,2138,atmospheric,1525283760 2944 | 567,2138,beautiful,1525283752 2945 | 567,2324,bittersweet,1525285789 2946 | 567,2324,emotional,1525285791 2947 | 567,2324,Heartwarming,1525285795 2948 | 567,2324,poignant,1525285800 2949 | 567,2324,sentimental,1525285793 2950 | 567,2324,tear jerker,1525285797 2951 | 567,2324,tearjerking,1525285787 2952 | 567,2731,emotional,1525285761 2953 | 567,2731,gentle,1525285768 2954 | 567,2731,heartwarming,1525285770 2955 | 567,2731,lyrical,1525285763 2956 | 567,2731,reflective,1525285753 2957 | 567,3134,downbeat,1525287634 2958 | 567,3134,poignant,1525287636 2959 | 567,3266,black comedy,1525283004 2960 | 567,3266,crazy,1525283002 2961 | 567,3266,dark,1525282999 2962 | 567,3266,dark comedy,1525282995 2963 | 567,3266,dark humor,1525283008 2964 | 567,3266,serial killer,1525283012 2965 | 567,3424,great cinematography,1525284096 2966 | 567,3671,dark humor,1525283215 2967 | 567,3671,easygoing,1525283227 2968 | 567,3671,silly,1525283225 2969 | 567,3676,art house,1525282858 2970 | 567,3676,Atmospheric,1525282843 2971 | 567,3676,creepy,1525282842 2972 | 567,3676,cryptic,1525282831 2973 | 567,3676,dark,1525282860 2974 | 567,3676,disturbing,1525282840 2975 | 567,3676,dreamlike,1525282845 2976 | 567,3676,enigmatic,1525282838 2977 | 567,3676,fucked up,1525282866 2978 | 567,3676,gruesome,1525282863 2979 | 567,3676,hallucinatory,1525282848 2980 | 567,3676,Insane,1525282856 2981 | 567,3676,paranoid,1525282868 2982 | 567,3676,strange,1525282854 2983 | 567,3676,surreal,1525282828 2984 | 567,3676,weird,1525282851 2985 | 567,3994,atmospheric,1525285498 2986 | 567,3994,somber,1525285503 2987 | 567,4144,atmospheric,1525283524 2988 | 567,4144,Beautiful,1525283566 2989 | 567,4144,bittersweet,1525283563 2990 | 567,4144,dreamy,1525283557 2991 | 567,4144,elegant,1525283529 2992 | 567,4144,heartbreaking,1525283540 2993 | 567,4144,intimate,1525283535 2994 | 567,4144,loneliness,1525283527 2995 | 567,4144,long takes,1525283572 2996 | 567,4144,longing,1525283542 2997 | 567,4144,melancholic,1525283526 2998 | 567,4144,melancholy,1525283531 2999 | 567,4144,moody,1525283533 3000 | 567,4144,nocturnal,1525283537 3001 | 567,4144,romantic,1525283568 3002 | 567,4144,Unique,1525283560 3003 | 567,4144,visually appealing,1525283544 3004 | 567,4144,visually stunning,1525283550 3005 | 567,4226,cerebral,1525282541 3006 | 567,4226,dreamlike,1525282545 3007 | 567,4552,"""artsy""",1525285878 3008 | 567,4552,atmospheric,1525285874 3009 | 567,4552,gritty,1525285880 3010 | 567,4552,hallucinatory,1525285875 3011 | 567,4552,surreal,1525285865 3012 | 567,4552,visually stunning,1525285872 3013 | 567,4878,atmospheric,1525282581 3014 | 567,4878,cerebral,1525282593 3015 | 567,4878,dreamlike,1525282577 3016 | 567,4878,enigmatic,1525282598 3017 | 567,4878,hallucinatory,1525282590 3018 | 567,4878,mental illness,1525282579 3019 | 567,4878,mindfuck,1525282602 3020 | 567,4878,philosophy,1525282583 3021 | 567,4878,psychological,1525282595 3022 | 567,4878,quirky,1525282588 3023 | 567,4878,surreal,1525282571 3024 | 567,4878,thought-provoking,1525282573 3025 | 567,4878,weird,1525282585 3026 | 567,5673,anger,1525282648 3027 | 567,5673,awkward,1525282635 3028 | 567,5673,bittersweet,1525282627 3029 | 567,5673,hallucinatory,1525282650 3030 | 567,5673,quirky,1525282629 3031 | 567,5673,sad,1525282646 3032 | 567,5673,surreal,1525282654 3033 | 567,5673,symbolism,1525282652 3034 | 567,5673,tense,1525282631 3035 | 567,5673,unconventional,1525282616 3036 | 567,5673,unique,1525282637 3037 | 567,5673,wistful,1525282643 3038 | 567,5690,beautiful,1525283799 3039 | 567,5690,downbeat,1525283801 3040 | 567,5690,grim,1525283797 3041 | 567,5690,poignant,1525283788 3042 | 567,5690,tear jerker,1525283804 3043 | 567,5690,tragedy,1525283795 3044 | 567,5690,tragic,1525283809 3045 | 567,6214,dark,1525283391 3046 | 567,6214,disturbing,1525283383 3047 | 567,6291,Depressing,1525287038 3048 | 567,6291,melancholy,1525287040 3049 | 567,6291,sad,1525287050 3050 | 567,6377,heartwarming,1525286742 3051 | 567,6669,meditative,1525286218 3052 | 567,6669,philosophical,1525286214 3053 | 567,6669,poignant,1525286220 3054 | 567,6669,purposefulness,1525286216 3055 | 567,6818,atmospheric,1525282332 3056 | 567,6818,bleak,1525282333 3057 | 567,6818,disturbing,1525282337 3058 | 567,6818,gritty,1525282340 3059 | 567,6818,harsh,1525282335 3060 | 567,7024,bleak,1525283990 3061 | 567,7024,confrontational,1525284016 3062 | 567,7024,disturbing,1525283975 3063 | 567,7024,harsh,1525283987 3064 | 567,7024,haunting,1525284009 3065 | 567,7024,inhumane,1525284013 3066 | 567,7024,thought-provoking,1525283979 3067 | 567,7361,arthouse,1525282744 3068 | 567,7361,artistic,1525282751 3069 | 567,7361,atmospheric,1525282731 3070 | 567,7361,beautiful,1525282670 3071 | 567,7361,bittersweet,1525282677 3072 | 567,7361,colourful,1525282696 3073 | 567,7361,comedy,1525282701 3074 | 567,7361,dreamlike,1525282681 3075 | 567,7361,emotional,1525282667 3076 | 567,7361,feel-good,1525282786 3077 | 567,7361,happpiness,1525282735 3078 | 567,7361,humane,1525282811 3079 | 567,7361,insightful,1525282724 3080 | 567,7361,intelligent,1525282746 3081 | 567,7361,lovely,1525282737 3082 | 567,7361,melancholy,1525282692 3083 | 567,7361,mind-bending,1525282728 3084 | 567,7361,philosophy,1525282673 3085 | 567,7361,quirky,1525282687 3086 | 567,7361,quirky romantic,1525282715 3087 | 567,7361,romantic,1525282694 3088 | 567,7361,surreal,1525282704 3089 | 567,7361,surrealism,1525282683 3090 | 567,7361,thought-provoking,1525282689 3091 | 567,8335,heartbreaking,1525285746 3092 | 567,8477,post-apocalyptic,1525282477 3093 | 567,25771,mindfuck,1525282469 3094 | 567,25771,surreal,1525282464 3095 | 567,25771,surrealism,1525282466 3096 | 567,26717,boring,1525285892 3097 | 567,26717,psychedelic,1525285894 3098 | 567,26717,symbolic,1525285896 3099 | 567,27773,bizarre,1525283311 3100 | 567,27773,claustrophobic,1525283317 3101 | 567,27773,depressing,1525283303 3102 | 567,27773,disturbing,1525283306 3103 | 567,27773,hallucinatory,1525283304 3104 | 567,27773,paranoid,1525283318 3105 | 567,27773,surreal,1525283313 3106 | 567,27773,twisted,1525283339 3107 | 567,30810,heartwarming,1525284057 3108 | 567,30810,off-beat comedy,1525284048 3109 | 567,30810,quirky,1525284031 3110 | 567,30810,trippy,1525284067 3111 | 567,30810,visually appealing,1525284041 3112 | 567,30810,whimsical,1525284034 3113 | 567,40491,depression,1525282384 3114 | 567,45880,cinematography,1525287347 3115 | 567,45880,lyrical,1525287351 3116 | 567,48394,atmospheric,1525286824 3117 | 567,48394,bittersweet,1525286830 3118 | 567,48394,visually appealing,1525286827 3119 | 567,48780,atmospheric,1525286470 3120 | 567,48780,enigmatic,1525286468 3121 | 567,50872,clever,1525283885 3122 | 567,50872,inspirational,1525283888 3123 | 567,56782,atmospheric,1525283248 3124 | 567,56782,cerebral,1525283239 3125 | 567,56782,character study,1525283288 3126 | 567,56782,gritty,1525283242 3127 | 567,56782,intense,1525283245 3128 | 567,56782,long shots,1525283253 3129 | 567,56782,morality,1525283268 3130 | 567,56782,visually appealing,1525283236 3131 | 567,57502,abstract,1525285566 3132 | 567,57502,dark,1525285567 3133 | 567,57502,psychedelic,1525285561 3134 | 567,57502,surreal,1525285558 3135 | 567,58301,dark,1525283356 3136 | 567,58301,dark humor,1525283360 3137 | 567,58301,intelligent,1525283358 3138 | 567,58301,thought-provoking,1525283367 3139 | 567,58559,dark,1525285594 3140 | 567,58559,gritty,1525285601 3141 | 567,63062,emotional,1525286194 3142 | 567,68954,bittersweet,1525286758 3143 | 567,68954,emotional,1525286759 3144 | 567,68954,heartbreaking,1525286755 3145 | 567,68954,touching,1525286762 3146 | 567,71899,bittersweet,1525283044 3147 | 567,71899,black comedy,1525283050 3148 | 567,71899,character study,1525283036 3149 | 567,71899,dark comedy,1525283033 3150 | 567,71899,friendship,1525283029 3151 | 567,71899,loneliness,1525283039 3152 | 567,71899,mental illness,1525283031 3153 | 567,71899,philosophical,1525283034 3154 | 567,71899,poignant,1525283043 3155 | 567,71899,quirky,1525283048 3156 | 567,71899,sad,1525283041 3157 | 567,71899,sweet,1525283052 3158 | 567,71899,touching,1525283027 3159 | 567,74791,surreal,1525286112 3160 | 567,74791,whimsical,1525286110 3161 | 567,79132,cerebral,1525287390 3162 | 567,79132,dreamlike,1525287383 3163 | 567,79132,philosophy,1525287385 3164 | 567,79132,thought-provoking,1525287380 3165 | 567,80463,good dialogue,1525286347 3166 | 567,80463,loneliness,1525286325 3167 | 567,80463,witty,1525286317 3168 | 567,89745,fun,1525285537 3169 | 567,89745,great humor,1525285534 3170 | 567,89745,visually appealing,1525285532 3171 | 567,94959,quirky,1525286360 3172 | 567,95558,Beautiful,1525287504 3173 | 567,95558,emotional,1525287499 3174 | 567,95558,poetic,1525287506 3175 | 567,96079,beautiful cinematography,1525287362 3176 | 567,96079,beautifully filmed,1525287364 3177 | 567,96079,cinematography,1525287366 3178 | 567,99764,moving,1525282453 3179 | 567,99764,philosopical,1525282451 3180 | 567,99764,surreal,1525282456 3181 | 567,99764,weird,1525282455 3182 | 567,99917,artistic,1525286643 3183 | 567,99917,artsy,1525286654 3184 | 567,99917,atmospheric,1525286652 3185 | 567,99917,Beautiful,1525286645 3186 | 567,99917,dreamlike,1525286649 3187 | 567,99917,existentialism,1525286647 3188 | 567,104875,quirky,1525285913 3189 | 567,104944,emotional,1525285982 3190 | 567,104944,feel-good,1525285975 3191 | 567,104944,heartwarming,1525285984 3192 | 567,104944,mental illness,1525285990 3193 | 567,104944,touching,1525285973 3194 | 567,105504,suspense,1525286930 3195 | 567,105504,tense,1525286932 3196 | 567,106766,atmospheric,1525283920 3197 | 567,106766,cinematography,1525283924 3198 | 567,106766,depressing,1525283919 3199 | 567,106766,funny,1525283917 3200 | 567,107406,fatalistic,1525286785 3201 | 567,108932,cheeky,1525283702 3202 | 567,108932,clever,1525283675 3203 | 567,108932,colorful,1525283676 3204 | 567,108932,feel-good,1525283700 3205 | 567,108932,fun,1525283672 3206 | 567,108932,imaginative,1525283681 3207 | 567,108932,quirky,1525283678 3208 | 567,109487,bad dialogue,1525287562 3209 | 567,109487,philosophical issues,1525287571 3210 | 567,109487,thought-provoking,1525287567 3211 | 567,109487,visually appealing,1525287559 3212 | 567,112421,adorable,1525283433 3213 | 567,112421,black comedy,1525283421 3214 | 567,112421,Eccentric,1525283427 3215 | 567,112421,introspection,1525283425 3216 | 567,112421,mental illness,1525283417 3217 | 567,112421,quirky,1525283419 3218 | 567,112552,inspirational,1525286276 3219 | 567,112552,inspiring,1525286278 3220 | 567,112552,intense,1525286274 3221 | 567,112852,funny,1525285382 3222 | 567,112852,Great Visuals,1525285333 3223 | 567,112852,humorous,1525285336 3224 | 567,112852,unlikely hero,1525285378 3225 | 567,113705,bittersweet,1525286093 3226 | 567,114627,atmospheric,1525286623 3227 | 567,114627,bizzare,1525286627 3228 | 567,114627,eerie,1525286621 3229 | 567,114627,symbolism,1525286619 3230 | 567,116797,inspirational,1525286868 3231 | 567,116797,intelligent,1525286865 3232 | 567,117877,philosophical,1525287615 3233 | 567,117877,trippy,1525287611 3234 | 567,117887,heartwarming,1525285938 3235 | 567,119145,slick,1525286728 3236 | 567,122882,beautiful,1525283844 3237 | 567,122882,cinematography,1525283853 3238 | 567,122882,visually appealing,1525283836 3239 | 567,122912,Dark,1525282411 3240 | 567,122912,emotional,1525282392 3241 | 567,122912,Sad,1525282400 3242 | 567,122912,Visually appealing,1525282405 3243 | 567,122912,Visually stunning,1525282403 3244 | 567,122916,awesome,1525285267 3245 | 567,122916,humor,1525285285 3246 | 567,122916,quirky,1525285291 3247 | 567,122916,unconventional,1525285265 3248 | 567,122916,white guilt,1525285281 3249 | 567,122918,fun,1525286132 3250 | 567,122922,visually appealing,1525287157 3251 | 567,127298,depressing,1525286249 3252 | 567,127298,understated,1525286256 3253 | 567,134130,smart,1525287677 3254 | 567,134170,funny,1525283492 3255 | 567,134170,ridiculous,1525283483 3256 | 567,134170,silly,1525283502 3257 | 567,138036,slick,1525287196 3258 | 567,138036,stylish,1525287190 3259 | 567,139385,cinematography,1525287409 3260 | 567,139385,visually appealing,1525287413 3261 | 567,139385,Visually Striking,1525287426 3262 | 567,139644,atmospheric,1525286568 3263 | 567,139644,tension,1525286565 3264 | 567,140174,moving,1525287480 3265 | 567,140174,touching,1525287482 3266 | 567,141890,Moving,1525286977 3267 | 567,143367,contemplative,1525283159 3268 | 567,143367,philosophical,1525283157 3269 | 567,143367,thought-provoking,1525283155 3270 | 567,143367,tragic,1525283168 3271 | 567,148626,funny,1525287708 3272 | 567,148626,interesting,1525287704 3273 | 567,148626,Witty,1525287702 3274 | 567,148881,Existential,1525282419 3275 | 567,148881,surreal,1525282422 3276 | 567,152077,creepy,1525287447 3277 | 567,152077,suspense,1525287443 3278 | 567,153070,nightmare,1525282902 3279 | 567,155288,suspense,1525286165 3280 | 567,155288,thought-provoking,1525286163 3281 | 567,156605,quirky,1525283453 3282 | 567,156605,sweet,1525283455 3283 | 567,156605,understated,1525283456 3284 | 567,161634,intense,1525287241 3285 | 567,164179,beautiful visuals,1525285690 3286 | 567,164179,Cerebral,1525285686 3287 | 567,164179,cinematography,1525285696 3288 | 567,164179,good cinematography,1525285706 3289 | 567,164179,smart,1525285699 3290 | 567,164179,thought-provoking,1525285688 3291 | 567,164179,visually appealing,1525285693 3292 | 567,164909,Bittersweet,1525287656 3293 | 567,164909,visually appealing,1525287658 3294 | 567,167746,funny,1525285825 3295 | 567,167746,heartwarming,1525285833 3296 | 567,168252,dark,1525283942 3297 | 567,168252,emotional,1525283946 3298 | 567,168252,gritty,1525283940 3299 | 567,168252,heartbreaking,1525283948 3300 | 567,168252,predictible plot,1525283957 3301 | 567,170945,paranoia,1525286504 3302 | 567,170945,Suspenseful,1525286501 3303 | 567,174055,brilliant,1525285239 3304 | 567,174055,inspiring,1525285236 3305 | 567,174055,tense,1525285229 3306 | 567,176371,atmospheric,1525283076 3307 | 567,176371,beautiful,1525283078 3308 | 567,176371,cinematography,1525283075 3309 | 567,176371,dark,1525283093 3310 | 567,176371,dreamlike,1525283084 3311 | 567,176371,existentialism,1525283080 3312 | 567,176371,moody,1525283086 3313 | 567,176371,philosophical,1525283089 3314 | 567,176419,allegorical,1525287584 3315 | 567,176419,uncomfortable,1525287588 3316 | 567,176419,unsettling,1525287586 3317 | 567,180031,atmospheric,1525284150 3318 | 567,180031,dreamlike,1525284163 3319 | 567,180985,bad music,1525285320 3320 | 573,248,bad,1186589145 3321 | 573,248,Sinbad,1186589145 3322 | 573,413,Comedy,1186588856 3323 | 573,540,bad,1186588897 3324 | 573,707,bad,1186588902 3325 | 573,836,seen at the cinema,1186588944 3326 | 573,1441,Not Seen,1186588856 3327 | 573,1779,good,1186588894 3328 | 573,1801,seen more than once,1186588917 3329 | 573,2116,classic,1186588944 3330 | 573,2431,bad,1186588902 3331 | 573,2662,classic,1186588962 3332 | 573,2951,bad,1186588901 3333 | 573,4015,bad,1186589039 3334 | 573,4343,really bad,1186589079 3335 | 573,4343,Seann William Scott,1186589079 3336 | 573,4446,sci-fi,1186588886 3337 | 573,4448,boring,1186589048 3338 | 573,5064,remake,1186589036 3339 | 573,5254,Great movie,1186589125 3340 | 573,5254,Wesley Snipes,1186589125 3341 | 573,6016,not seen,1186588853 3342 | 573,6157,bad,1186589133 3343 | 573,6157,Ben Affleck,1186589133 3344 | 573,6373,classic,1186588855 3345 | 573,35836,BEST PICTURE,1186589105 3346 | 573,35836,classic,1186589105 3347 | 573,35836,hilarious,1186589105 3348 | 573,35836,steve carell,1186589105 3349 | 573,52712,HORRIBLE ACTING,1186722048 3350 | 573,52712,interesting,1186722060 3351 | 599,293,Action,1498456142 3352 | 599,293,assassin,1498456106 3353 | 599,293,assassination,1498456192 3354 | 599,293,assassins,1498456184 3355 | 599,293,awkward romance,1498456167 3356 | 599,293,corruption,1498456155 3357 | 599,293,crime,1498456141 3358 | 599,293,disturbing,1498456144 3359 | 599,293,drama,1498456149 3360 | 599,293,French,1498456150 3361 | 599,293,friendship,1498456139 3362 | 599,293,Gary Oldman,1498456130 3363 | 599,293,great acting,1498456128 3364 | 599,293,Guns,1498456179 3365 | 599,293,hit men,1498456165 3366 | 599,293,hitman,1498456113 3367 | 599,293,humorous,1498456177 3368 | 599,293,imdb top 250,1498456175 3369 | 599,293,Jean Reno,1498456121 3370 | 599,293,Lolita theme,1498456188 3371 | 599,293,loneliness,1498456186 3372 | 599,293,love story,1498456132 3373 | 599,293,Luc Besson,1498456137 3374 | 599,293,Natalie Portman,1498456126 3375 | 599,293,organized crime,1498456152 3376 | 599,293,police,1498456181 3377 | 599,293,police corruption,1498456159 3378 | 599,293,quirky,1498456135 3379 | 599,293,sniper,1498456182 3380 | 599,293,tense,1498456190 3381 | 599,293,touching,1498456147 3382 | 599,293,unique,1498456157 3383 | 599,296,1990s,1498456537 3384 | 599,296,achronological,1498456475 3385 | 599,296,action,1498456356 3386 | 599,296,action packed,1498456535 3387 | 599,296,aggressive,1498456533 3388 | 599,296,amazing,1498456678 3389 | 599,296,amazing dialogues,1498456473 3390 | 599,296,anthology,1498456531 3391 | 599,296,assassin,1498456382 3392 | 599,296,atmospheric,1498456376 3393 | 599,296,AWESOME,1498456559 3394 | 599,296,bad ass,1498456471 3395 | 599,296,bad language,1498456469 3396 | 599,296,bad-ass,1498456676 3397 | 599,296,bible,1498456675 3398 | 599,296,biblical references,1498456529 3399 | 599,296,big boys with guns,1498456673 3400 | 599,296,big name actors,1498456671 3401 | 599,296,Black comedy,1498456350 3402 | 599,296,black humor,1498456481 3403 | 599,296,black humour,1498456670 3404 | 599,296,blood,1498456448 3405 | 599,296,blood splatters,1498456467 3406 | 599,296,bloody,1498456431 3407 | 599,296,bruce willis,1498456352 3408 | 599,296,brutality,1498456543 3409 | 599,296,casual violence,1498456669 3410 | 599,296,character development,1498456527 3411 | 599,296,characters,1498456466 3412 | 599,296,classic,1498456360 3413 | 599,296,classic movie,1498456667 3414 | 599,296,coke,1498456666 3415 | 599,296,comedy,1498456378 3416 | 599,296,conversation,1498456524 3417 | 599,296,cool,1498456405 3418 | 599,296,cool style,1498456664 3419 | 599,296,crime,1498456353 3420 | 599,296,crime scene scrubbing,1498456663 3421 | 599,296,cult,1498456391 3422 | 599,296,cult classic,1498456522 3423 | 599,296,cult film,1498456345 3424 | 599,296,dance,1498456661 3425 | 599,296,dancing,1498456464 3426 | 599,296,dark,1498456404 3427 | 599,296,dark comedy,1498456339 3428 | 599,296,dark humor,1498456385 3429 | 599,296,dialogue,1498456395 3430 | 599,296,different,1498456660 3431 | 599,296,diner,1498456658 3432 | 599,296,disjointed timeline,1498456444 3433 | 599,296,disturbing,1498456521 3434 | 599,296,drama,1498456561 3435 | 599,296,drug overdose,1498456656 3436 | 599,296,drugs,1498456348 3437 | 599,296,drugs & music,1498456655 3438 | 599,296,ensemble cast,1498456419 3439 | 599,296,entertaining,1498456519 3440 | 599,296,entirely dialogue,1498456429 3441 | 599,296,episodic,1498456517 3442 | 599,296,exciting,1498456427 3443 | 599,296,fast paced,1498456436 3444 | 599,296,fast-paced,1498456515 3445 | 599,296,film noir,1498456653 3446 | 599,296,film-noir,1498456622 3447 | 599,296,foul language,1498456686 3448 | 599,296,fun,1498456421 3449 | 599,296,funny,1498456383 3450 | 599,296,gangster,1498456389 3451 | 599,296,gangsters,1498456406 3452 | 599,296,genius,1498456589 3453 | 599,296,golden watch,1498456513 3454 | 599,296,good dialogue,1498456372 3455 | 599,296,good music,1498456396 3456 | 599,296,gore,1498456511 3457 | 599,296,great acting,1498456509 3458 | 599,296,great dialogue,1498456402 3459 | 599,296,great soundtrack,1498456380 3460 | 599,296,gritty,1498456426 3461 | 599,296,guns,1498456450 3462 | 599,296,Harvey Keitel,1498456684 3463 | 599,296,heroin,1498456483 3464 | 599,296,Highly quotable,1498456400 3465 | 599,296,hit men,1498456412 3466 | 599,296,hitman,1498456505 3467 | 599,296,homosexuality,1498456562 3468 | 599,296,humour,1498456564 3469 | 599,296,iconic,1498456488 3470 | 599,296,imdb top 250,1498456388 3471 | 599,296,innovative,1498456490 3472 | 599,296,intellectual,1498456565 3473 | 599,296,intelligent,1498456462 3474 | 599,296,intense,1498456411 3475 | 599,296,interesting,1498456541 3476 | 599,296,intertwining storylines,1498456460 3477 | 599,296,interwoven storylines,1498456567 3478 | 599,296,ironic,1498456491 3479 | 599,296,irony,1498456458 3480 | 599,296,John Travolta,1498456374 3481 | 599,296,killer-as-protagonist,1498456494 3482 | 599,296,los angeles,1498456569 3483 | 599,296,Mafia,1498456414 3484 | 599,296,masterpiece,1498456371 3485 | 599,296,meaningless violence,1498456571 3486 | 599,296,milkshake,1498456572 3487 | 599,296,mobster,1498456574 3488 | 599,296,mobsters,1498456575 3489 | 599,296,monologue,1498456577 3490 | 599,296,motherfucker,1498456497 3491 | 599,296,multiple stories,1498456498 3492 | 599,296,multiple storylines,1498456343 3493 | 599,296,neo-noir,1498456486 3494 | 599,296,noir,1498456408 3495 | 599,296,non-linear,1498456367 3496 | 599,296,non-linear timeline,1498456578 3497 | 599,296,nonlinear,1498456341 3498 | 599,296,nonlinear narrative,1498456457 3499 | 599,296,nonlinear storyline,1498456580 3500 | 599,296,nonlinear timeline,1498456581 3501 | 599,296,notable soundtrack,1498456393 3502 | 599,296,offensive,1498456583 3503 | 599,296,organised crime,1498456585 3504 | 599,296,organized crime,1498456363 3505 | 599,296,original,1498456479 3506 | 599,296,original plot,1498456455 3507 | 599,296,out of order,1498456586 3508 | 599,296,Palme d'Or,1498456690 3509 | 599,296,parody,1498456591 3510 | 599,296,philosophical,1498456500 3511 | 599,296,pop culture references,1498456478 3512 | 599,296,psychological,1498456593 3513 | 599,296,pulp,1498456484 3514 | 599,296,Quentin Tarantino,1498456338 3515 | 599,296,quirky,1498456364 3516 | 599,296,Quotable,1498456446 3517 | 599,296,r:disturbing violent content including rape,1498456502 3518 | 599,296,r:disturbing violent images,1498456595 3519 | 599,296,r:graphic sexuality,1498456597 3520 | 599,296,r:some violence,1498456599 3521 | 599,296,r:strong bloody violence,1498456601 3522 | 599,296,r:strong language,1498456452 3523 | 599,296,r:sustained strong stylized violence,1498456603 3524 | 599,296,r:violence,1498456424 3525 | 599,296,random,1498456503 3526 | 599,296,rape,1498456417 3527 | 599,296,retro,1498456442 3528 | 599,296,Roger Avary,1498456682 3529 | 599,296,royal with cheese,1498456610 3530 | 599,296,Samuel L. Jackson,1498456342 3531 | 599,296,sarcasm,1498456612 3532 | 599,296,satire,1498456614 3533 | 599,296,sexy,1498456615 3534 | 599,296,smart writing,1498456617 3535 | 599,296,sophisticated,1498456618 3536 | 599,296,soundtrack,1498456439 3537 | 599,296,splatter,1498456620 3538 | 599,296,Steve Buscemi,1498456694 3539 | 599,296,storytelling,1498456375 3540 | 599,296,stylish,1498456398 3541 | 599,296,stylized,1498456354 3542 | 599,296,suspense,1498456680 3543 | 599,296,Tarantino,1498456358 3544 | 599,296,thought-provoking,1498456638 3545 | 599,296,thriller,1498456415 3546 | 599,296,travolta,1498456539 3547 | 599,296,twist ending,1498456639 3548 | 599,296,Uma Thurman,1498456370 3549 | 599,296,unique,1498456641 3550 | 599,296,unpredictable,1498456476 3551 | 599,296,unusual,1498456642 3552 | 599,296,very funny,1498456434 3553 | 599,296,violence,1498456347 3554 | 599,296,violent,1498456366 3555 | 599,296,witty,1498456437 3556 | 599,924,aliens,1498456783 3557 | 599,924,apes,1498456822 3558 | 599,924,Arthur C. Clarke,1498456786 3559 | 599,924,artificial intelligence,1498456752 3560 | 599,924,atmospheric,1498456757 3561 | 599,924,cinematography,1498456766 3562 | 599,924,classic,1498456774 3563 | 599,924,computer,1498456793 3564 | 599,924,confusing ending,1498456779 3565 | 599,924,cult film,1498456771 3566 | 599,924,Dull,1498456811 3567 | 599,924,future,1498456770 3568 | 599,924,futuristic,1498456776 3569 | 599,924,imdb top 250,1498456804 3570 | 599,924,masterpiece,1498456762 3571 | 599,924,meditative,1498456768 3572 | 599,924,music,1498456773 3573 | 599,924,mystery,1498456802 3574 | 599,924,Oscar (Best Effects - Visual Effects),1498456791 3575 | 599,924,overrated,1498456813 3576 | 599,924,philosophical,1498456759 3577 | 599,924,relaxing,1498456798 3578 | 599,924,revolutionary,1498456794 3579 | 599,924,robots,1498456781 3580 | 599,924,sci-fi,1498456751 3581 | 599,924,setting:space/space ship,1498456796 3582 | 599,924,slow,1498456765 3583 | 599,924,slow paced,1498456806 3584 | 599,924,soundtrack,1498456785 3585 | 599,924,space,1498456756 3586 | 599,924,space travel,1498456790 3587 | 599,924,spacecraft,1498456788 3588 | 599,924,Stanley Kubrick,1498456754 3589 | 599,924,superb soundtrack,1498456825 3590 | 599,924,surreal,1498456763 3591 | 599,924,technology,1498456827 3592 | 599,924,tedious,1498456814 3593 | 599,924,visual,1498456799 3594 | 599,924,visually appealing,1498456760 3595 | 599,1732,black comedy,1498456261 3596 | 599,1732,bowling,1498456271 3597 | 599,1732,classic,1498456304 3598 | 599,1732,coen brothers,1498456256 3599 | 599,1732,comedy,1498456268 3600 | 599,1732,crime,1498456292 3601 | 599,1732,Cult classic,1498456280 3602 | 599,1732,cult film,1498456260 3603 | 599,1732,dark comedy,1498456258 3604 | 599,1732,deadpan,1498456303 3605 | 599,1732,drugs,1498456286 3606 | 599,1732,funny,1498456291 3607 | 599,1732,great dialogue,1498456264 3608 | 599,1732,great soundtrack,1498456283 3609 | 599,1732,Highly quotable,1498456296 3610 | 599,1732,imdb top 250,1498456301 3611 | 599,1732,Jeff Bridges,1498456269 3612 | 599,1732,John Goodman,1498456278 3613 | 599,1732,Julianne Moore,1498456294 3614 | 599,1732,kidnapping,1498456299 3615 | 599,1732,marijuana,1498456281 3616 | 599,1732,Nudity (Full Frontal),1498456288 3617 | 599,1732,off-beat comedy,1498456273 3618 | 599,1732,Philip Seymour Hoffman,1498456275 3619 | 599,1732,quirky,1498456263 3620 | 599,1732,ransom,1498456308 3621 | 599,1732,rug,1498456306 3622 | 599,1732,sarcasm,1498456285 3623 | 599,1732,satirical,1498456266 3624 | 599,1732,Steve Buscemi,1498456276 3625 | 599,1732,violence,1498456297 3626 | 599,2959,action,1498456930 3627 | 599,2959,atmospheric,1498456906 3628 | 599,2959,based on a book,1498456916 3629 | 599,2959,Brad Pitt,1498456893 3630 | 599,2959,challenging,1498456963 3631 | 599,2959,Chuck Palahniuk,1498456949 3632 | 599,2959,classic,1498456925 3633 | 599,2959,clever,1498456960 3634 | 599,2959,complicated,1498456923 3635 | 599,2959,consumerism,1498456965 3636 | 599,2959,crime,1498456935 3637 | 599,2959,dark,1498456928 3638 | 599,2959,dark comedy,1498456891 3639 | 599,2959,David Fincher,1498456926 3640 | 599,2959,disturbing,1498456908 3641 | 599,2959,double life,1498456958 3642 | 599,2959,Edward Norton,1498456896 3643 | 599,2959,fighting,1498456937 3644 | 599,2959,great acting,1498456951 3645 | 599,2959,helena bonham carter,1498456939 3646 | 599,2959,imaginary friend,1498456945 3647 | 599,2959,imdb top 250,1498456941 3648 | 599,2959,mental illness,1498456903 3649 | 599,2959,mind-blowing,1498456955 3650 | 599,2959,mindfuck,1498456912 3651 | 599,2959,narrated,1498456934 3652 | 599,2959,Nudity (Topless),1498456962 3653 | 599,2959,Palahnuik,1498456948 3654 | 599,2959,philosophical,1498456911 3655 | 599,2959,philosophy,1498456898 3656 | 599,2959,postmodern,1498456954 3657 | 599,2959,powerful ending,1498456921 3658 | 599,2959,psychological,1498456909 3659 | 599,2959,psychological thriller,1498456953 3660 | 599,2959,psychology,1498456890 3661 | 599,2959,quirky,1498456918 3662 | 599,2959,satirical,1498456920 3663 | 599,2959,schizophrenia,1498456967 3664 | 599,2959,social commentary,1498456894 3665 | 599,2959,societal criticism,1498456946 3666 | 599,2959,stylized,1498456932 3667 | 599,2959,surreal,1498456900 3668 | 599,2959,TERRORISM,1498456966 3669 | 599,2959,thought-provoking,1498456901 3670 | 599,2959,twist,1498456943 3671 | 599,2959,twist ending,1498456888 3672 | 599,2959,violence,1498456904 3673 | 599,2959,violent,1498456914 3674 | 600,273,gothic,1237739064 3675 | 606,1357,music,1176765393 3676 | 606,1948,British,1177512649 3677 | 606,3578,Romans,1173212944 3678 | 606,5694,70mm,1175638092 3679 | 606,6107,World War II,1178473747 3680 | 606,7382,for katie,1171234019 3681 | 606,7936,austere,1173392334 3682 | 610,3265,gun fu,1493843984 3683 | 610,3265,heroic bloodshed,1493843978 3684 | 610,168248,Heroic Bloodshed,1493844270 3685 | --------------------------------------------------------------------------------