Installation

pip install tensorflow-recommenders

Documentation

Quick start

fromtypingimportDict,Textimporttensorflowastfimporttensorflow_datasetsastfdsimporttensorflow_recommendersastfrs# Ratings data.ratings=tfds.load('movielens/100k-ratings',split="train")# Features of all the available movies.movies=tfds.load('movielens/100k-movies',split="train")# Select the basic features.ratings=ratings.map(lambdax: {"movie_id":tf.strings.to_number(x["movie_id"]),"user_id":tf.strings.to_number(x["user_id"])})movies=movies.map(lambdax:tf.strings.to_number(x["movie_id"]))# Build a model.classModel(tfrs.Model):def__init__(self):super().__init__()# Set up user representation.self.user_model=tf.keras.layers.Embedding(input_dim=2000,output_dim=64)# Set up movie representation.self.item_model=tf.keras.layers.Embedding(input_dim=2000,output_dim=64)# Set up a retrieval task and evaluation metrics over the# entire dataset of candidates.self.task=tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(candidates=movies.batch(128).map(self.item_model)        )    )defcompute_loss(self,features:Dict[Text,tf.Tensor],training=False)->tf.Tensor:user_embeddings=self.user_model(features["user_id"])movie_embeddings=self.item_model(features["movie_id"])returnself.task(user_embeddings,movie_embeddings)model=Model()model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))# Randomly shuffle data and split between train and test.tf.random.set_seed(42)shuffled=ratings.shuffle(100_000,seed=42,reshuffle_each_iteration=False)train=shuffled.take(80_000)test=shuffled.skip(80_000).take(20_000)# Train.model.fit(train.batch(4096),epochs=5)# Evaluate.model.evaluate(test.batch(4096),return_dict=True)