├── data ├── ml-100k │ ├── u.info │ ├── u.item │ ├── u.genre │ ├── u.occupation │ ├── mku.sh │ ├── allbut.pl │ ├── README │ └── u.user ├── ml-100k.zip └── ml_small │ └── more │ ├── README.txt │ └── tags.csv ├── myplot.png ├── README.md ├── CFGAN.py ├── evaluation.py ├── model.py ├── data.py └── train.py /data/ml-100k/u.info: -------------------------------------------------------------------------------- 1 | 943 users 2 | 1682 items 3 | 100000 ratings 4 | -------------------------------------------------------------------------------- /myplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1051003502/CFGAN/HEAD/myplot.png -------------------------------------------------------------------------------- /data/ml-100k.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1051003502/CFGAN/HEAD/data/ml-100k.zip -------------------------------------------------------------------------------- /data/ml-100k/u.item: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1051003502/CFGAN/HEAD/data/ml-100k/u.item -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CFGAN-based-on-pytorch- 2 | A pytorch implementation of CFGAN 3 | paper title: CFGAN:A Generic Collaborative Filtering Framework based on Generative Adversarial Networks 4 | 5 | # run demo 6 | python CFGAN.py 7 | 8 | 9 | -------------------------------------------------------------------------------- /data/ml-100k/u.genre: -------------------------------------------------------------------------------- 1 | unknown|0 2 | Action|1 3 | Adventure|2 4 | Animation|3 5 | Children's|4 6 | Comedy|5 7 | Crime|6 8 | Documentary|7 9 | Drama|8 10 | Fantasy|9 11 | Film-Noir|10 12 | Horror|11 13 | Musical|12 14 | Mystery|13 15 | Romance|14 16 | Sci-Fi|15 17 | Thriller|16 18 | War|17 19 | Western|18 20 | 21 | -------------------------------------------------------------------------------- /data/ml-100k/u.occupation: -------------------------------------------------------------------------------- 1 | administrator 2 | artist 3 | doctor 4 | educator 5 | engineer 6 | entertainment 7 | executive 8 | healthcare 9 | homemaker 10 | lawyer 11 | librarian 12 | marketing 13 | none 14 | other 15 | programmer 16 | retired 17 | salesman 18 | scientist 19 | student 20 | technician 21 | writer 22 | -------------------------------------------------------------------------------- /CFGAN.py: -------------------------------------------------------------------------------- 1 | import data 2 | import train 3 | if __name__ == '__main__': 4 | trainSet, userCount, itemCount = data.loadTrainingData("data/ml-100k/u2.base", "\t") 5 | userCount = 943 + 1 6 | itemCount = 1682 + 1 7 | testSet, GroundTruth = data.loadTestData("data/ml-100k/u2.test", "\t") 8 | userList_test = list(testSet.keys()) 9 | trainVector, testMaskVector, batchCount = data.to_Vectors(trainSet, userCount, itemCount, userList_test, "userBased") 10 | train.main(trainSet,userCount,itemCount,testSet,GroundTruth,trainVector,testMaskVector,batchCount,1000,0.5,0.7,0.03) -------------------------------------------------------------------------------- /data/ml-100k/mku.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | trap `rm -f tmp.$$; exit 1` 1 2 15 4 | 5 | for i in 1 2 3 4 5 6 | do 7 | head -`expr $i \* 20000` u.data | tail -20000 > tmp.$$ 8 | sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.test 9 | head -`expr \( $i - 1 \) \* 20000` u.data > tmp.$$ 10 | tail -`expr \( 5 - $i \) \* 20000` u.data >> tmp.$$ 11 | sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.base 12 | done 13 | 14 | allbut.pl ua 1 10 100000 u.data 15 | sort -t" " -k 1,1n -k 2,2n ua.base > tmp.$$ 16 | mv tmp.$$ ua.base 17 | sort -t" " -k 1,1n -k 2,2n ua.test > tmp.$$ 18 | mv tmp.$$ ua.test 19 | 20 | allbut.pl ub 11 20 100000 u.data 21 | sort -t" " -k 1,1n -k 2,2n ub.base > tmp.$$ 22 | mv tmp.$$ ub.base 23 | sort -t" " -k 1,1n -k 2,2n ub.test > tmp.$$ 24 | mv tmp.$$ ub.test 25 | 26 | -------------------------------------------------------------------------------- /evaluation.py: -------------------------------------------------------------------------------- 1 | ''' 2 | 衡量预测效果 3 | groundTruth:真实结果 4 | result:预测向量 此量经过了+testMaskVector操作处理 已经剔除掉训练集中用户有过反映的量 5 | topN：取几个预测结果 6 | ''' 7 | import math 8 | def computeTopNAccuracy(groundTruth,result,topN): 9 | result=result.tolist() 10 | for i in range(len(result)): 11 | result[i]=(result[i],i) 12 | result.sort(key=lambda x:x[0],reverse=True) 13 | #print(result) 14 | hit=0 15 | dcg = 0 16 | idcg = 0 17 | idcgCount=len(groundTruth) 18 | for i in range(topN): 19 | if(result[i][1] in groundTruth): 20 | hit=hit+1 21 | dcg+=1/math.log2(i+2) 22 | if(idcgCount>0): 23 | idcg += 1/math.log2(i+2) 24 | idcgCount-=1 25 | return hit/topN,hit/len(groundTruth),dcg/idcg 26 | #computeTopNAccuracy(0,[1,2,5,9,100,-5,6,0],0) -------------------------------------------------------------------------------- /data/ml-100k/allbut.pl: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin/perl 2 | 3 | # get args 4 | if (@ARGV < 3) { 5 | print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n"; 6 | exit 1; 7 | } 8 | $basename = shift; 9 | $start = shift; 10 | $stop = shift; 11 | $maxtest = shift; 12 | 13 | # open files 14 | open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n"; 15 | open( BASEFILE, ">$basename.base" ) or die "Cannot open $basename.base for writing\n"; 16 | 17 | # init variables 18 | $testcnt = 0; 19 | 20 | while (<>) { 21 | ($user) = split; 22 | if (! defined $ratingcnt{$user}) { 23 | $ratingcnt{$user} = 0; 24 | } 25 | ++$ratingcnt{$user}; 26 | if (($testcnt < $maxtest || $maxtest <= 0) 27 | && $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) { 28 | ++$testcnt; 29 | print TESTFILE; 30 | } 31 | else { 32 | print BASEFILE; 33 | } 34 | } 35 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torchvision 4 | import torch.nn.functional as F 5 | from torchvision import datasets 6 | from torchvision import transforms 7 | from torchvision.utils import save_image 8 | from torch.autograd import Variable 9 | class discriminator(nn.Module): 10 | def __init__(self,itemCount): 11 | super(discriminator,self).__init__() 12 | self.dis=nn.Sequential( 13 | nn.Linear(itemCount*2,125), 14 | 15 | nn.ReLU(True), 16 | 17 | nn.Linear(125,1), 18 | nn.Sigmoid() 19 | ) 20 | def forward(self,data,condition): 21 | 22 | result=self.dis( torch.cat((data,condition),1) ) 23 | return result 24 | class generator(nn.Module): 25 | def __init__(self,itemCount): 26 | super(generator,self).__init__() 27 | self.gen=nn.Sequential( 28 | nn.Linear(itemCount,400), 29 | nn.ReLU(True), 30 | 31 | nn.Linear(400, 400), 32 | nn.ReLU(True), 33 | nn.Linear(400, 400), 34 | nn.ReLU(True), 35 | nn.Linear(400, 400), 36 | nn.ReLU(True), 37 | nn.Linear(400, itemCount), 38 | nn.Sigmoid() 39 | ) 40 | '''''' 41 | def forward(self,x): 42 | result=self.gen(x) 43 | return result -------------------------------------------------------------------------------- /data.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | """ 4 | Created on Mar. 11, 2019. 5 | tensorflow implementation of the paper: 6 | Dong-Kyu Chae et al. "CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks," In Proc. of ACM CIKM, 2018. 7 | @author: Dong-Kyu Chae (kyu899@agape.hanyang.ac.kr) 8 | 9 | IMPORTANT: make sure that (1) the user & item indices start from 0, and (2) the index should be continuous, without any empy index. 10 | """ 11 | 12 | import random 13 | import operator 14 | import numpy as np 15 | import codecs 16 | from collections import defaultdict 17 | from operator import itemgetter 18 | import collections 19 | import torch 20 | from torch.autograd import variable 21 | def splitData(): 22 | random.seed(0) 23 | print("start") 24 | fp1 = open("data/ml_small/train.csv", mode="w") 25 | fp2 = open("data/ml_small/test.csv", mode="w") 26 | for line in open("data/ml_small/ratings.csv"): 27 | if (random.randint(0, 8) == 0): 28 | fp2.writelines(line) 29 | else: 30 | fp1.writelines(line) 31 | print("end") 32 | fp1.close() 33 | fp2.close() 34 | 35 | '''加载训练集 36 | trainFile str 训练集文件名 37 | splitMark str 文件一行中的分隔符 38 | ''' 39 | def loadTrainingData(trainFile,splitMark): 40 | #trainFile = path + "/" + benchmark + "/" + benchmark + ".train" 41 | print(trainFile) 42 | 43 | trainSet = defaultdict(list) #字典默认值是一个list trainSet['key_new'] 是个list 44 | max_u_id = -1 45 | max_i_id = -1 46 | 47 | for line in open(trainFile): 48 | userId, itemId, rating,_ = line.strip().split(splitMark) 49 | 50 | userId = int(userId) 51 | itemId = int(itemId) 52 | 53 | # note that we regard all the observed ratings as implicit feedback 54 | trainSet[userId].append(itemId) 55 | 56 | max_u_id = max(userId, max_u_id) 57 | max_i_id = max(itemId, max_i_id) 58 | 59 | for u, i_list in trainSet.items(): 60 | i_list.sort() 61 | 62 | userCount = max_u_id + 1 63 | itemCount = max_i_id + 1 64 | 65 | print(userCount) 66 | print(itemCount) 67 | 68 | print("Training data loading done: %d users, %d items" % (userCount, itemCount)) 69 | 70 | return trainSet, userCount, itemCount #此处userCount itemCount并不能代表真实值 因为可能小于测试集合中的userCount itemCount 71 | 72 | ''' 73 | 装载测试集数据 74 | testSet [1:[...],2[...], ...] defaultdict(list) 75 | GroundTruth [[],[], ...] 只装着item 76 | ''' 77 | def loadTestData(testFile,splitMark): 78 | testSet = defaultdict(list) 79 | for line in open(testFile): 80 | userId, itemId, rating,_ = line.strip().split(splitMark) 81 | userId = int(userId) 82 | itemId = int(itemId) 83 | 84 | # note that we regard all the ratings in the test set as ground truth 85 | testSet[userId].append(itemId) 86 | 87 | GroundTruth = [] 88 | for u, i_list in testSet.items(): 89 | tmp = [] 90 | for j in i_list: 91 | tmp.append(j) 92 | 93 | GroundTruth.append(tmp) 94 | 95 | print("Test data loading done") 96 | 97 | return testSet, GroundTruth 98 | 99 | ''' 返回量trainVector testMaskvector batchCount 100 | testMaskVector 与trainVector相对应 -99999对应1 101 | testMaskVector + 预测结果 （然后取TOP N 相当于去掉了本来就是1的item 拿到了真实有用的预测item） 102 | ''' 103 | def to_Vectors(trainSet, userCount, itemCount, userList_test, mode): 104 | # assume that the default is itemBased 105 | 106 | testMaskDict = defaultdict(lambda: [0] * itemCount) 107 | batchCount = userCount #改动 直接写成userCount 108 | if mode == "itemBased":#改动 itemCount userCount互换 batchCount是物品数 109 | userCount = itemCount 110 | itemCount = batchCount 111 | batchCount = userCount 112 | 113 | trainDict = defaultdict(lambda: [0] * itemCount) 114 | 115 | for userId, i_list in trainSet.items(): 116 | for itemId in i_list: 117 | testMaskDict[userId][itemId] = -99999 118 | if mode == "userBased": 119 | trainDict[userId][itemId] = 1.0 120 | else: 121 | trainDict[itemId][userId] = 1.0 122 | 123 | trainVector = [] 124 | for batchId in range(batchCount): 125 | trainVector.append(trainDict[batchId]) 126 | 127 | testMaskVector = [] 128 | for userId in userList_test: 129 | testMaskVector.append(testMaskDict[userId]) 130 | 131 | print("Converting to vectors done....") 132 | 133 | return (torch.Tensor(trainVector)), torch.Tensor(testMaskVector), batchCount 134 | def getItemCount(fileName): 135 | itemCount=0 136 | for line in open(fileName): 137 | L=line.split(",") 138 | itemCount=max(itemCount,int(L[1])) 139 | return itemCount 140 | 141 | if __name__=="__main__": 142 | trainSet, userCount, itemCount=loadTrainingData("data/ml-100k/u1.base","\t") 143 | userCount=943+1 144 | itemCount=1682+1 145 | testSet, GroundTruth=loadTestData("data/ml-100k/u1.test","\t") 146 | userList_test = list(testSet.keys()) 147 | trainVector,testMaskVector,batchCount=to_Vectors(trainSet,userCount,itemCount,userList_test,"userBased") 148 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.autograd import Variable 4 | import model 5 | import random 6 | import evaluation 7 | import copy 8 | 9 | import numpy as np 10 | import matplotlib.pyplot as plt 11 | import matplotlib.patches as mpatches 12 | 13 | def paint(x,y1,y2,y3): 14 | plt.title("CFGAN") 15 | plt.xlabel('epoch') 16 | #plt.ylabel('') 17 | plt.plot(x, y2, "k-o", color='red', label='recall', markersize='0') 18 | plt.plot(x, y1, "k-o",color='black',label='precision',markersize = '0' )#List,List,List 19 | 20 | plt.plot(x, y3, "k-o",color='green',label='ndcg',markersize = '0') 21 | plt.ylim([0, 0.5]) 22 | plt.legend() # 图例 23 | plt.rcParams['lines.linewidth'] = 1 24 | plt.show() 25 | 26 | 27 | def main(trainSet,userCount,itemCount,testSet,GroundTruth,trainVector,testMaskVector,batchCount,epochCount,pro_ZR,pro_PM,arfa): 28 | X=[] #画图数据的保存 29 | precisionList=[] 30 | recallList=[] 31 | ndcgList=[] 32 | G=model.generator(itemCount) 33 | D=model.discriminator(itemCount) 34 | G = G.cuda() 35 | D = D.cuda() 36 | criterion1 = nn.BCELoss() # 二分类的交叉熵 37 | criterion2 = nn.MSELoss(size_average=False) 38 | d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0001) 39 | 40 | g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0001) 41 | 42 | G_step=2 43 | D_step=2 44 | batchSize_G = 32 45 | batchSize_D = 32 46 | realLabel_G = (torch.ones(batchSize_G)).cuda() 47 | fakeLabel_G = (torch.zeros(batchSize_G)).cuda() 48 | realLabel_D = (torch.ones(batchSize_D)).cuda() 49 | fakeLabel_D = (torch.zeros(batchSize_D)).cuda() 50 | ZR = [] 51 | PM = [] 52 | for epoch in range(epochCount): #训练epochCount次 53 | 54 | if(epoch%1==0): 55 | ZR = [] 56 | PM = [] 57 | for i in range(userCount): 58 | ZR.append([]) 59 | PM.append([]) 60 | ZR[i].append(np.random.choice(itemCount,int(pro_ZR*itemCount),replace=False)) 61 | PM[i].append(np.random.choice(itemCount,int(pro_PM*itemCount),replace=False)) 62 | for step in range(D_step):#训练D 63 | #maskVector1是PM方法的体现 这里要进行优化 减少程序消耗的内存 64 | 65 | leftIndex=random.randint(1,userCount-batchSize_D-1) 66 | realData=(trainVector[leftIndex:leftIndex+batchSize_D]).cuda() #MD个数据成为待训练数据 67 | 68 | maskVector1 = (trainVector[leftIndex:leftIndex+batchSize_D]).cuda() 69 | for i in range(len(maskVector1)): 70 | maskVector1[i][PM[leftIndex+i]]=1 71 | Condition=realData#把用户反馈数据作为他的特征 后期还要加入用户年龄、性别等信息 72 | realData_result=D(realData,Condition) 73 | d_loss_real=criterion1(realData_result,realLabel_D) 74 | 75 | fakeData=G(realData) 76 | fakeData=fakeData*maskVector1 77 | fakeData_result=D(fakeData,realData) 78 | d_loss_fake=criterion1(fakeData_result,fakeLabel_D) 79 | 80 | d_loss=d_loss_real+d_loss_fake 81 | d_optimizer.zero_grad() 82 | d_loss.backward() 83 | d_optimizer.step() 84 | 85 | for step in range(G_step):#训练G0 86 | #调整maskVector2\3 87 | leftIndex = random.randint(1, userCount - batchSize_G - 1) 88 | realData = (trainVector[leftIndex:leftIndex + batchSize_G]).cuda() 89 | 90 | maskVector2 = (trainVector[leftIndex:leftIndex + batchSize_G]).cuda() 91 | maskVector3 = (trainVector[leftIndex:leftIndex + batchSize_G]).cuda() 92 | for i in range(len(maskVector2)): 93 | maskVector2[i][PM[i+leftIndex]] = 1 94 | maskVector3[i][ZR[i+leftIndex]] = 1 95 | fakeData=G(realData) 96 | g_loss2=arfa * criterion2(fakeData, maskVector3) 97 | fakeData=fakeData*maskVector2 98 | g_fakeData_result=D(fakeData,realData) 99 | g_loss1=criterion1(g_fakeData_result,realLabel_G) 100 | g_loss=g_loss1+g_loss2 101 | g_optimizer.zero_grad() 102 | g_loss.backward() 103 | g_optimizer.step() 104 | 105 | if( epoch%10==0): 106 | 107 | hit=0 108 | peopleAmount=len(GroundTruth) 109 | recommendAmount=10 110 | index=0 111 | precisions=0 112 | recalls=0 113 | ndcgs=0 114 | for testUser in testSet.keys(): 115 | data = (trainVector[testUser]).cuda() 116 | 117 | result = G(data) + (testMaskVector[index]).cuda() 118 | index+=1 119 | precision,recall,ndcg=evaluation.computeTopNAccuracy(testSet[testUser], result, recommendAmount) 120 | precisions+=precision 121 | recalls+=recall 122 | ndcgs+=ndcg 123 | 124 | precisions /= peopleAmount 125 | recalls /= peopleAmount 126 | ndcgs /= peopleAmount 127 | 128 | precisionList.append(precisions) 129 | recallList.append(recalls) 130 | ndcgList.append(ndcgs) 131 | 132 | X.append(epoch) 133 | print('Epoch[{}/{}],d_loss:{:.6f},g_loss:{:.6f},precision:{},recall:{},ndcg:{}'.format(epoch, epochCount, 134 | d_loss.item(), 135 | g_loss.item(), 136 | precisions,recalls,ndcgs)) 137 | paint(X,precisionList,recallList,ndcgList) 138 | return precisionList 139 | 140 | 141 | -------------------------------------------------------------------------------- /data/ml-100k/README: -------------------------------------------------------------------------------- 1 | SUMMARY & USAGE LICENSE 2 | ============================================= 3 | 4 | MovieLens data sets were collected by the GroupLens Research Project 5 | at the University of Minnesota. 6 | 7 | This data set consists of: 8 | * 100,000 ratings (1-5) from 943 users on 1682 movies. 9 | * Each user has rated at least 20 movies. 10 | * Simple demographic info for the users (age, gender, occupation, zip) 11 | 12 | The data was collected through the MovieLens web site 13 | (movielens.umn.edu) during the seven-month period from September 19th, 14 | 1997 through April 22nd, 1998. This data has been cleaned up - users 15 | who had less than 20 ratings or did not have complete demographic 16 | information were removed from this data set. Detailed descriptions of 17 | the data file can be found at the end of this file. 18 | 19 | Neither the University of Minnesota nor any of the researchers 20 | involved can guarantee the correctness of the data, its suitability 21 | for any particular purpose, or the validity of results based on the 22 | use of the data set. The data set may be used for any research 23 | purposes under the following conditions: 24 | 25 | * The user may not state or imply any endorsement from the 26 | University of Minnesota or the GroupLens Research Group. 27 | 28 | * The user must acknowledge the use of the data set in 29 | publications resulting from the use of the data set 30 | (see below for citation information). 31 | 32 | * The user may not redistribute the data without separate 33 | permission. 34 | 35 | * The user may not use this information for any commercial or 36 | revenue-bearing purposes without first obtaining permission 37 | from a faculty member of the GroupLens Research Project at the 38 | University of Minnesota. 39 | 40 | If you have any further questions or comments, please contact GroupLens 41 | . 42 | 43 | CITATION 44 | ============================================== 45 | 46 | To acknowledge use of the dataset in publications, please cite the 47 | following paper: 48 | 49 | F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: 50 | History and Context. ACM Transactions on Interactive Intelligent 51 | Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. 52 | DOI=http://dx.doi.org/10.1145/2827872 53 | 54 | 55 | ACKNOWLEDGEMENTS 56 | ============================================== 57 | 58 | Thanks to Al Borchers for cleaning up this data and writing the 59 | accompanying scripts. 60 | 61 | PUBLISHED WORK THAT HAS USED THIS DATASET 62 | ============================================== 63 | 64 | Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic 65 | Framework for Performing Collaborative Filtering. Proceedings of the 66 | 1999 Conference on Research and Development in Information 67 | Retrieval. Aug. 1999. 68 | 69 | FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT 70 | ============================================== 71 | 72 | The GroupLens Research Project is a research group in the Department 73 | of Computer Science and Engineering at the University of Minnesota. 74 | Members of the GroupLens Research Project are involved in many 75 | research projects related to the fields of information filtering, 76 | collaborative filtering, and recommender systems. The project is lead 77 | by professors John Riedl and Joseph Konstan. The project began to 78 | explore automated collaborative filtering in 1992, but is most well 79 | known for its world wide trial of an automated collaborative filtering 80 | system for Usenet news in 1996. The technology developed in the 81 | Usenet trial formed the base for the formation of Net Perceptions, 82 | Inc., which was founded by members of GroupLens Research. Since then 83 | the project has expanded its scope to research overall information 84 | filtering solutions, integrating in content-based methods as well as 85 | improving current collaborative filtering technology. 86 | 87 | Further information on the GroupLens Research project, including 88 | research publications, can be found at the following web site: 89 | 90 | http://www.grouplens.org/ 91 | 92 | GroupLens Research currently operates a movie recommender based on 93 | collaborative filtering: 94 | 95 | http://www.movielens.org/ 96 | 97 | DETAILED DESCRIPTIONS OF DATA FILES 98 | ============================================== 99 | 100 | Here are brief descriptions of the data. 101 | 102 | ml-data.tar.gz -- Compressed tar file. To rebuild the u data files do this: 103 | gunzip ml-data.tar.gz 104 | tar xvf ml-data.tar 105 | mku.sh 106 | 107 | u.data -- The full u data set, 100000 ratings by 943 users on 1682 items. 108 | Each user has rated at least 20 movies. Users and items are 109 | numbered consecutively from 1. The data is randomly 110 | ordered. This is a tab separated list of 111 | user id | item id | rating | timestamp. 112 | The time stamps are unix seconds since 1/1/1970 UTC 113 | 114 | u.info -- The number of users, items, and ratings in the u data set. 115 | 116 | u.item -- Information about the items (movies); this is a tab separated 117 | list of 118 | movie id | movie title | release date | video release date | 119 | IMDb URL | unknown | Action | Adventure | Animation | 120 | Children's | Comedy | Crime | Documentary | Drama | Fantasy | 121 | Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi | 122 | Thriller | War | Western | 123 | The last 19 fields are the genres, a 1 indicates the movie 124 | is of that genre, a 0 indicates it is not; movies can be in 125 | several genres at once. 126 | The movie ids are the ones used in the u.data data set. 127 | 128 | u.genre -- A list of the genres. 129 | 130 | u.user -- Demographic information about the users; this is a tab 131 | separated list of 132 | user id | age | gender | occupation | zip code 133 | The user ids are the ones used in the u.data data set. 134 | 135 | u.occupation -- A list of the occupations. 136 | 137 | u1.base -- The data sets u1.base and u1.test through u5.base and u5.test 138 | u1.test are 80%/20% splits of the u data into training and test data. 139 | u2.base Each of u1, ..., u5 have disjoint test sets; this if for 140 | u2.test 5 fold cross validation (where you repeat your experiment 141 | u3.base with each training and test set and average the results). 142 | u3.test These data sets can be generated from u.data by mku.sh. 143 | u4.base 144 | u4.test 145 | u5.base 146 | u5.test 147 | 148 | ua.base -- The data sets ua.base, ua.test, ub.base, and ub.test 149 | ua.test split the u data into a training set and a test set with 150 | ub.base exactly 10 ratings per user in the test set. The sets 151 | ub.test ua.test and ub.test are disjoint. These data sets can 152 | be generated from u.data by mku.sh. 153 | 154 | allbut.pl -- The script that generates training and test sets where 155 | all but n of a users ratings are in the training data. 156 | 157 | mku.sh -- A shell script to generate all the u data sets from u.data. 158 | -------------------------------------------------------------------------------- /data/ml_small/more/README.txt: -------------------------------------------------------------------------------- 1 | Summary 2 | ======= 3 | 4 | This dataset (ml-latest-small) describes 5-star rating and free-text tagging activity from [MovieLens](http://movielens.org), a movie recommendation service. It contains 100836 ratings and 3683 tag applications across 9742 movies. These data were created by 610 users between March 29, 1996 and September 24, 2018. This dataset was generated on September 26, 2018. 5 | 6 | Users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided. 7 | 8 | The data are contained in the files `links.csv`, `movies.csv`, `ratings.csv` and `tags.csv`. More details about the contents and use of all these files follows. 9 | 10 | This is a *development* dataset. As such, it may change over time and is not an appropriate dataset for shared research results. See available *benchmark* datasets if that is your intent. 11 | 12 | This and other GroupLens data sets are publicly available for download at . 13 | 14 | 15 | Usage License 16 | ============= 17 | 18 | Neither the University of Minnesota nor any of the researchers involved can guarantee the correctness of the data, its suitability for any particular purpose, or the validity of results based on the use of the data set. The data set may be used for any research purposes under the following conditions: 19 | 20 | * The user may not state or imply any endorsement from the University of Minnesota or the GroupLens Research Group. 21 | * The user must acknowledge the use of the data set in publications resulting from the use of the data set (see below for citation information). 22 | * The user may redistribute the data set, including transformations, so long as it is distributed under these same license conditions. 23 | * The user may not use this information for any commercial or revenue-bearing purposes without first obtaining permission from a faculty member of the GroupLens Research Project at the University of Minnesota. 24 | * The executable software scripts are provided "as is" without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the quality and performance of them is with you. Should the program prove defective, you assume the cost of all necessary servicing, repair or correction. 25 | 26 | In no event shall the University of Minnesota, its affiliates or employees be liable to you for any damages arising out of the use or inability to use these programs (including but not limited to loss of data or data being rendered inaccurate). 27 | 28 | If you have any further questions or comments, please email 29 | 30 | 31 | Citation 32 | ======== 33 | 34 | To acknowledge use of the dataset in publications, please cite the following paper: 35 | 36 | > F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. 37 | 38 | 39 | Further Information About GroupLens 40 | =================================== 41 | 42 | GroupLens is a research group in the Department of Computer Science and Engineering at the University of Minnesota. Since its inception in 1992, GroupLens's research projects have explored a variety of fields including: 43 | 44 | * recommender systems 45 | * online communities 46 | * mobile and ubiquitious technologies 47 | * digital libraries 48 | * local geographic information systems 49 | 50 | GroupLens Research operates a movie recommender based on collaborative filtering, MovieLens, which is the source of these data. We encourage you to visit to try it out! If you have exciting ideas for experimental work to conduct on MovieLens, send us an email at - we are always interested in working with external collaborators. 51 | 52 | 53 | Content and Use of Files 54 | ======================== 55 | 56 | Formatting and Encoding 57 | ----------------------- 58 | 59 | The dataset files are written as [comma-separated values](http://en.wikipedia.org/wiki/Comma-separated_values) files with a single header row. Columns that contain commas (`,`) are escaped using double-quotes (`"`). These files are encoded as UTF-8. If accented characters in movie titles or tag values (e.g. Misérables, Les (1995)) display incorrectly, make sure that any program reading the data, such as a text editor, terminal, or script, is configured for UTF-8. 60 | 61 | 62 | User Ids 63 | -------- 64 | 65 | MovieLens users were selected at random for inclusion. Their ids have been anonymized. User ids are consistent between `ratings.csv` and `tags.csv` (i.e., the same id refers to the same user across the two files). 66 | 67 | 68 | Movie Ids 69 | --------- 70 | 71 | Only movies with at least one rating or tag are included in the dataset. These movie ids are consistent with those used on the MovieLens web site (e.g., id `1` corresponds to the URL ). Movie ids are consistent between `ratings.csv`, `tags.csv`, `movies.csv`, and `links.csv` (i.e., the same id refers to the same movie across these four data files). 72 | 73 | 74 | Ratings Data File Structure (ratings.csv) 75 | ----------------------------------------- 76 | 77 | 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: 78 | 79 | userId,movieId,rating,timestamp 80 | 81 | The lines within this file are ordered first by userId, then, within user, by movieId. 82 | 83 | Ratings are made on a 5-star scale, with half-star increments (0.5 stars - 5.0 stars). 84 | 85 | Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970. 86 | 87 | 88 | Tags Data File Structure (tags.csv) 89 | ----------------------------------- 90 | 91 | 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: 92 | 93 | userId,movieId,tag,timestamp 94 | 95 | The lines within this file are ordered first by userId, then, within user, by movieId. 96 | 97 | Tags are user-generated metadata about movies. Each tag is typically a single word or short phrase. The meaning, value, and purpose of a particular tag is determined by each user. 98 | 99 | Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970. 100 | 101 | 102 | Movies Data File Structure (movies.csv) 103 | --------------------------------------- 104 | 105 | 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: 106 | 107 | movieId,title,genres 108 | 109 | Movie titles are entered manually or imported from , and include the year of release in parentheses. Errors and inconsistencies may exist in these titles. 110 | 111 | Genres are a pipe-separated list, and are selected from the following: 112 | 113 | * Action 114 | * Adventure 115 | * Animation 116 | * Children's 117 | * Comedy 118 | * Crime 119 | * Documentary 120 | * Drama 121 | * Fantasy 122 | * Film-Noir 123 | * Horror 124 | * Musical 125 | * Mystery 126 | * Romance 127 | * Sci-Fi 128 | * Thriller 129 | * War 130 | * Western 131 | * (no genres listed) 132 | 133 | 134 | Links Data File Structure (links.csv) 135 | --------------------------------------- 136 | 137 | 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: 138 | 139 | movieId,imdbId,tmdbId 140 | 141 | movieId is an identifier for movies used by . E.g., the movie Toy Story has the link . 142 | 143 | imdbId is an identifier for movies used by . E.g., the movie Toy Story has the link . 144 | 145 | tmdbId is an identifier for movies used by . E.g., the movie Toy Story has the link . 146 | 147 | Use of the resources listed above is subject to the terms of each provider. 148 | 149 | 150 | Cross-Validation 151 | ---------------- 152 | 153 | Prior versions of the MovieLens dataset included either pre-computed cross-folds or scripts to perform this computation. We no longer bundle either of these features with the dataset, since most modern toolkits provide this as a built-in feature. If you wish to learn about standard approaches to cross-fold computation in the context of recommender systems evaluation, see [LensKit](http://lenskit.org) for tools, documentation, and open-source code examples. 154 | -------------------------------------------------------------------------------- /data/ml-100k/u.user: -------------------------------------------------------------------------------- 1 | 1|24|M|technician|85711 2 | 2|53|F|other|94043 3 | 3|23|M|writer|32067 4 | 4|24|M|technician|43537 5 | 5|33|F|other|15213 6 | 6|42|M|executive|98101 7 | 7|57|M|administrator|91344 8 | 8|36|M|administrator|05201 9 | 9|29|M|student|01002 10 | 10|53|M|lawyer|90703 11 | 11|39|F|other|30329 12 | 12|28|F|other|06405 13 | 13|47|M|educator|29206 14 | 14|45|M|scientist|55106 15 | 15|49|F|educator|97301 16 | 16|21|M|entertainment|10309 17 | 17|30|M|programmer|06355 18 | 18|35|F|other|37212 19 | 19|40|M|librarian|02138 20 | 20|42|F|homemaker|95660 21 | 21|26|M|writer|30068 22 | 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-------------------------------------------------------------------------------- 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 | --------------------------------------------------------------------------------