├── models ├── __init__.py ├── matrix_factorization.py ├── neural_collaborative_filtering.py ├── base_model.py └── attention_collaborative_filtering.py ├── utils ├── __init__.py ├── model_config.py └── data_utils.py ├── config └── main.ini ├── requirements ├── requirements-cpu.txt └── requirements-gpu.txt ├── .gitattributes ├── datasets ├── pinterest │ ├── test.rating │ ├── test.negative │ └── train.rating └── movie_lens │ ├── test.negative │ ├── test.rating │ └── train.rating ├── README.md ├── .gitignore ├── run.py └── LICENSE /models/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /config/main.ini: -------------------------------------------------------------------------------- 1 | [TRAINING] 2 | num_epochs = 10 3 | batch_size = 512 4 | learning_rate = 0.001 5 | -------------------------------------------------------------------------------- /requirements/requirements-cpu.txt: -------------------------------------------------------------------------------- 1 | pandas==0.22.0 2 | numpy==1.13.1 3 | tqdm==4.19.9 4 | tensorflow==1.12.2 5 | -------------------------------------------------------------------------------- /requirements/requirements-gpu.txt: -------------------------------------------------------------------------------- 1 | pandas==0.22.0 2 | numpy==1.13.1 3 | tqdm==4.19.9 4 | tensorflow-gpu==1.5.0 5 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | *.negative filter=lfs diff=lfs merge=lfs -text 2 | *.rating filter=lfs diff=lfs merge=lfs -text 3 | -------------------------------------------------------------------------------- /datasets/pinterest/test.rating: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:3a8a8f7eb8e21cc5928b8259dd2c265269ecfa219d92ba829249956aa293d732 3 | size 807267 4 | -------------------------------------------------------------------------------- /datasets/movie_lens/test.negative: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:127853228a76b7d21c0481e8ef8d00dd009cd5c88670dbdd9e9debfdb9f93de4 3 | size 2891424 4 | -------------------------------------------------------------------------------- /datasets/movie_lens/test.rating: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:231ae11eb060b643a8c58c063cd17ffc743b54f569afb073d1abb6cf126b88b5 3 | size 128039 4 | -------------------------------------------------------------------------------- /datasets/movie_lens/train.rating: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:7fb2920a6977c7e6f16517e76ffabebca7b5558a8189436e2c37bbb824532cd7 3 | size 20982911 4 | -------------------------------------------------------------------------------- /datasets/pinterest/test.negative: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:392d6561b4c4fbb6f095349e56e5fa3e58214d087b3f4cdc523e5cc618909704 3 | size 27404899 4 | -------------------------------------------------------------------------------- /datasets/pinterest/train.rating: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:7b0a8286dfe12ac7241d4e6cc830f372277d6d80f01ff68873dc366c9354b5a4 3 | size 21138451 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![](https://img.shields.io/badge/status-under--dev-red.svg?style=plastic) ![](https://img.shields.io/badge/TensorFlow-1.5.0-blue.svg?style=plastic) ![](https://img.shields.io/badge/Python-3.6-blue.svg?style=plastic) 2 | 3 | # Neural recommender system implementation in Tensorflow [under-dev] 4 | 5 | This repository mainly implements neural recommender models in TensorFlow proposed in the following paper: 6 | 7 | Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua (2017). Neural Collaborative Filtering. In Proceedings of WWW '17, Perth, Australia, April 03-07, 2017. 8 | -------------------------------------------------------------------------------- /utils/model_config.py: -------------------------------------------------------------------------------- 1 | from enum import Enum 2 | 3 | from models import attention_collaborative_filtering, matrix_factorization, neural_collaborative_filtering 4 | 5 | 6 | class AttentionType(Enum): 7 | additive = 0, 8 | multiplicative = 1 9 | 10 | 11 | class ModelType(Enum): 12 | matrix_factor = 0, 13 | ncf = 1, 14 | acf = 2 15 | 16 | 17 | MODEL = { 18 | ModelType.matrix_factor.name: matrix_factorization.MatrixFactorization, 19 | ModelType.ncf.name: neural_collaborative_filtering.NeuralCollaborativeFiltering, 20 | ModelType.acf.name: attention_collaborative_filtering.AttentionCollaborativeFiltering 21 | } 22 | -------------------------------------------------------------------------------- /models/matrix_factorization.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | from models.base_model import BaseRecommenderModel 4 | 5 | 6 | class MatrixFactorization(BaseRecommenderModel): 7 | 8 | def __init__(self, num_users, num_items, user_embedding_size=50, item_embedding_size=50, learning_rate=0.001): 9 | super().__init__(num_users, num_items, user_embedding_size, item_embedding_size, learning_rate) 10 | 11 | def model_implementation(self, users_embedded, items_embedded): 12 | with tf.name_scope('factorization'): 13 | users_items = tf.multiply(users_embedded, items_embedded) 14 | prediction = tf.layers.dense(users_items, units=1, activation=tf.nn.sigmoid) 15 | return prediction -------------------------------------------------------------------------------- /models/neural_collaborative_filtering.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | from models.base_model import BaseRecommenderModel 4 | 5 | 6 | class NeuralCollaborativeFiltering(BaseRecommenderModel): 7 | 8 | def __init__(self, num_users, num_items, user_embedding_size=50, item_embedding_size=50, learning_rate=0.001): 9 | super().__init__(num_users, num_items, user_embedding_size, item_embedding_size, learning_rate) 10 | 11 | def model_implementation(self, users_embedded, items_embedded): 12 | layers_sizes = [200, 100] 13 | with tf.name_scope('ncf'): 14 | users_items = tf.concat([users_embedded, items_embedded], axis=1) 15 | 16 | output = users_items 17 | for i, layer_size in enumerate(layers_sizes): 18 | output = tf.layers.dense(output, units=layers_sizes[i], activation=tf.nn.relu) 19 | 20 | prediction = tf.squeeze(tf.layers.dense(output, units=1, activation=tf.nn.sigmoid)) 21 | return prediction 22 | -------------------------------------------------------------------------------- /models/base_model.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | 4 | class BaseRecommenderModel: 5 | 6 | def __init__(self, num_users, num_items, user_embedding_size=50, item_embedding_size=50, learning_rate=0.001): 7 | self.users = tf.placeholder(dtype=tf.int32, shape=[None]) 8 | self.items = tf.placeholder(dtype=tf.int32, shape=[None]) 9 | 10 | self.ratings = tf.placeholder(dtype=tf.float32, shape=[None]) 11 | 12 | with tf.name_scope('embeddings'): 13 | users_embeddings = tf.get_variable('users_embeddings', [num_users, user_embedding_size]) 14 | items_embeddings = tf.get_variable('items_embeddings', [num_items, item_embedding_size]) 15 | 16 | users_embedded = tf.gather(users_embeddings, self.users) 17 | items_embedded = tf.gather(items_embeddings, self.items) 18 | 19 | with tf.name_scope('model'): 20 | self.prediction = self.model_implementation(users_embedded, items_embedded) 21 | 22 | with tf.name_scope('loss'): 23 | self.loss = tf.losses.sigmoid_cross_entropy(self.ratings, self.prediction) 24 | self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.loss) 25 | 26 | with tf.name_scope('metrics'): 27 | tf.summary.scalar("loss", self.loss) 28 | self.summary_op = tf.summary.merge_all() 29 | 30 | def model_implementation(self, users_embedded, items_embedded): 31 | raise NotImplementedError 32 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | .idea 103 | -------------------------------------------------------------------------------- /models/attention_collaborative_filtering.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | from models.base_model import BaseRecommenderModel 4 | 5 | 6 | class AttentionCollaborativeFiltering(BaseRecommenderModel): 7 | 8 | def __init__(self, num_users, num_items, user_embedding_size=50, item_embedding_size=50, learning_rate=0.001): 9 | super().__init__(num_users, num_items, user_embedding_size, item_embedding_size, learning_rate) 10 | 11 | def model_implementation(self, users_embedded, items_embedded): 12 | layers_sizes = [200, 100] 13 | with tf.name_scope('acf'): 14 | users_self_attention, _ = multiplicative_attention(users_embedded, users_embedded, users_embedded) 15 | items_self_attention, _ = multiplicative_attention(items_embedded, items_embedded, items_embedded) 16 | users_items_attention, _ = multiplicative_attention(users_embedded, items_embedded, 17 | items_embedded) # TODO check correctness 18 | users_items = tf.concat([users_self_attention, items_self_attention, users_items_attention], axis=0) 19 | 20 | output = users_items 21 | for i, layer_size in enumerate(layers_sizes): 22 | output = tf.layers.dense(output, units=layers_sizes[i], activation=tf.nn.relu) 23 | 24 | prediction = tf.layers.dense(output, units=1, activation=tf.nn.sigmoid) 25 | return prediction 26 | 27 | 28 | def multiplicative_attention(queries, keys, values, model_size=None): 29 | if model_size is None: 30 | model_size = tf.to_float(queries.get_shape().as_list()[-1]) 31 | 32 | keys_T = tf.transpose(keys, [0, 2, 1]) 33 | Q_K = tf.matmul(queries, keys_T) / tf.sqrt(model_size) 34 | attentions_weights = tf.nn.softmax(Q_K) 35 | multiplicative_att = tf.matmul(attentions_weights, values) 36 | return multiplicative_att, attentions_weights 37 | 38 | 39 | def additive_attention(query, keys, values): 40 | raise NotImplementedError 41 | -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | import configparser 2 | import logging 3 | from argparse import ArgumentParser 4 | from tqdm import tqdm 5 | 6 | import tensorflow as tf 7 | 8 | from utils.data_utils import prepare_experiment, EXPERIMENTS 9 | from utils.model_config import MODEL 10 | 11 | logging.basicConfig(level=logging.INFO) 12 | logger = logging.getLogger(__name__) 13 | 14 | 15 | def train(model, experiment_name, main_config): 16 | model_dir = 'model' 17 | 18 | experiment = EXPERIMENTS[experiment_name] 19 | num_items = experiment.num_items() 20 | num_users = experiment.num_users() 21 | 22 | model = model(num_users, 23 | num_items, 24 | user_embedding_size=50, 25 | item_embedding_size=50) 26 | 27 | logger.info('Loaded {} model'.format(model)) 28 | training_df = prepare_experiment(model_dir, experiment_name) 29 | user_input, item_input, labels = training_df['users'].as_matrix(), training_df['items'].as_matrix(), \ 30 | training_df['labels'].as_matrix() 31 | 32 | logger.info('Loaded {} experiment'.format(experiment)) 33 | 34 | num_epochs = int(main_config['TRAINING']['num_epochs']) 35 | batch_size = int(main_config['TRAINING']['batch_size']) 36 | 37 | num_batches = len(labels) // batch_size 38 | with tf.Session() as session: 39 | summary_writer = tf.summary.FileWriter('{}/{}/test/'.format(model_dir, 'recommender'), graph=session.graph) 40 | session.run(tf.global_variables_initializer()) 41 | 42 | global_step = 0 43 | for epoch in tqdm(range(num_epochs), desc='Epochs'): 44 | # shuffle data 45 | 46 | tqdm_iter = tqdm(range(num_batches), total=num_batches, desc="Batches", leave=False) 47 | for batch in range(num_batches): 48 | global_step += 1 49 | user_batch = user_input[batch * batch_size: (batch + 1) * batch_size] 50 | item_batch = item_input[batch * batch_size: (batch + 1) * batch_size] 51 | labels_batch = labels[batch * batch_size: (batch + 1) * batch_size] 52 | feed_dict = {model.users: user_batch, model.items: item_batch, model.ratings: labels_batch} 53 | loss, opt, summary_op = session.run([model.loss, model.optimizer, model.summary_op], feed_dict=feed_dict) 54 | summary_writer.add_summary(summary_op, global_step) 55 | if batch % 10 == 0: 56 | pass # make eval 57 | 58 | tqdm_iter.set_postfix( 59 | loss='{:.2f}'.format(float(loss)), 60 | barch='{}|{}'.format(batch, num_batches), 61 | epoch=epoch) 62 | 63 | 64 | def main(): 65 | parser = ArgumentParser() 66 | parser.add_argument('model', 67 | choices=['mf', 'ncf', 'acf'], 68 | help='model to be used') 69 | parser.add_argument('experiment', 70 | choices=['MovieLens', 'Pinterest'], 71 | help='experiment to be used') 72 | args = parser.parse_args() 73 | 74 | main_config = configparser.ConfigParser() 75 | main_config.read('config/main.ini') 76 | 77 | model = MODEL[args.model] 78 | 79 | train(model, args.experiment, main_config) 80 | 81 | 82 | if __name__ == '__main__': 83 | main() 84 | -------------------------------------------------------------------------------- /utils/data_utils.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os 3 | from enum import Enum 4 | 5 | import numpy as np 6 | import pandas as pd 7 | 8 | logging.basicConfig(level=logging.INFO) 9 | logger = logging.getLogger(__name__) 10 | 11 | 12 | class Experiments(Enum): 13 | MovieLens = 0, 14 | Pinterest = 1 15 | 16 | 17 | DATASETS_PATS = { 18 | Experiments.MovieLens.name: 'datasets/movie_lens', 19 | Experiments.Pinterest.name: 'datasets/pinterest' 20 | } 21 | 22 | 23 | class RecommenderExperiment: 24 | column_names = ['Users', 'Items', 'Ratings', 'Ids'] 25 | 26 | def __init__(self): 27 | self.train_data = pd.read_csv('{}/{}'.format(self.dataset_path(), self.train_fn()), sep='\t', 28 | names=self.column_names) 29 | self.test_data = pd.read_csv('{}/{}'.format(self.dataset_path(), self.test_fn()), sep='\t', 30 | names=self.column_names) 31 | self.negative_data = pd.read_csv('{}/{}'.format(self.dataset_path(), self.negative_fn()), sep='\t') 32 | 33 | def dataset_path(self): 34 | raise NotImplementedError 35 | 36 | def train_fn(self): 37 | raise NotImplementedError 38 | 39 | def test_fn(self): 40 | raise NotImplementedError 41 | 42 | def negative_fn(self): 43 | raise NotImplementedError 44 | 45 | def train(self): 46 | return self.train_data 47 | 48 | def test(self): 49 | return self.test_data 50 | 51 | def negative(self): 52 | return self.negative_data 53 | 54 | def num_users(self): 55 | return max(self.train_data['Users']) + 1 56 | 57 | def num_items(self): 58 | return max(self.train_data['Items']) + 1 59 | 60 | 61 | class MovieLens(RecommenderExperiment): 62 | 63 | def dataset_path(self): 64 | return DATASETS_PATS[Experiments.MovieLens.name] 65 | 66 | def train_fn(self): 67 | return 'train.rating' 68 | 69 | def test_fn(self): 70 | return 'test.rating' 71 | 72 | def negative_fn(self): 73 | return 'test.negative' 74 | 75 | 76 | class Pinterest(RecommenderExperiment): 77 | 78 | def dataset_path(self): 79 | return DATASETS_PATS[Experiments.Pinterest.name] 80 | 81 | def train_fn(self): 82 | return 'train.rating' 83 | 84 | def test_fn(self): 85 | return 'test.rating' 86 | 87 | def negative_fn(self): 88 | return 'test.negative' 89 | 90 | 91 | EXPERIMENTS = { 92 | Experiments.MovieLens.name: MovieLens(), 93 | Experiments.Pinterest.name: Pinterest() 94 | } 95 | 96 | 97 | def prepare_experiment(model_dir, experiment: str, num_negatives=2): 98 | dataset = EXPERIMENTS[experiment] 99 | train_with_negatives = get_train_instances(dataset, num_negatives) 100 | return train_with_negatives 101 | 102 | 103 | def get_train_instances(dataset, num_negatives, force=False): 104 | """ 105 | Randomly generates negatives samples for each training example. 106 | :param dataset: 107 | :param num_negatives: 108 | :param force: 109 | :return: 110 | """ 111 | dataset_path = dataset.dataset_path() 112 | train_data = dataset.train() 113 | num_items = dataset.num_items() 114 | if force: # generate data and save it to file 115 | training_data = _create_training_file(dataset_path, train_data, num_items, num_negatives) 116 | else: # read data from file 117 | file_to_load = '{}/training_data.csv'.format(dataset_path) 118 | training_data = pd.read_csv(file_to_load) 119 | logger.info('Loaded training data from: %s', file_to_load) 120 | return training_data 121 | 122 | 123 | def _create_training_file(model_dir, train, num_items, num_negatives): 124 | os.makedirs(model_dir, exist_ok=True) 125 | user_input, item_input, labels = [], [], [] 126 | users_items = train[['Users', 'Items']].as_matrix() 127 | logger.info('Corpus contains %d samples, generating negatives will take a while...', len(train)) 128 | logger.info('Generating training instances...') 129 | for user, item in users_items: 130 | # positive instance 131 | user_input.append(user) 132 | item_input.append(item) 133 | labels.append(1) 134 | # negative instances 135 | user_positive_items = users_items[users_items[:, 0] == user, 1] 136 | for t in range(num_negatives): 137 | sample_item = np.random.randint(num_items) 138 | while sample_item in user_positive_items: 139 | sample_item = np.random.randint(num_items) 140 | user_input.append(user) 141 | item_input.append(sample_item) 142 | labels.append(0) 143 | training_data = pd.DataFrame({'users': user_input, 'items': item_input, 'labels': labels}) 144 | file_to_save = '{}/training_data.csv'.format(model_dir) 145 | training_data.to_csv(file_to_save, index=False) 146 | logger.info('Saved training data to: %s', file_to_save) 147 | logger.info('Finished generating training instances.') 148 | return training_data -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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