In this tutorial, we build a simple matrix factorization model using the MovieLens 100K dataset with TFRS. We can use this model to recommend movies for a given user.

Import TFRS

from typing import Dict, Textimport numpy as npimport tensorflow as tfimport tensorflow_datasets as tfdsimport tensorflow_recommenders as tfrs

Read the data

# 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(lambda x: {    "movie_title": x["movie_title"],    "user_id": x["user_id"]})movies = movies.map(lambda x: x["movie_title"])

Build vocabularies to convert user ids and movie titles into integer indices for embedding layers:

user_ids_vocabulary = tf.keras.layers.experimental.preprocessing.StringLookup(mask_token=None)user_ids_vocabulary.adapt(ratings.map(lambda x: x["user_id"]))movie_titles_vocabulary = tf.keras.layers.experimental.preprocessing.StringLookup(mask_token=None)movie_titles_vocabulary.adapt(movies)

Define a model

We can define a TFRS model by inheriting from tfrs.Model and implementing the compute_loss method:

class MovieLensModel(tfrs.Model):  # We derive from a custom base class to help reduce boilerplate. Under the hood,  # these are still plain Keras Models.  def __init__(      self,      user_model: tf.keras.Model,      movie_model: tf.keras.Model,      task: tfrs.tasks.Retrieval):    super().__init__()    # Set up user and movie representations.    self.user_model = user_model    self.movie_model = movie_model    # Set up a retrieval task.    self.task = task  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:    # Define how the loss is computed.    user_embeddings = self.user_model(features["user_id"])    movie_embeddings = self.movie_model(features["movie_title"])    return self.task(user_embeddings, movie_embeddings)

Define the two models and the retrieval task.

# Define user and movie models.user_model = tf.keras.Sequential([    user_ids_vocabulary,    tf.keras.layers.Embedding(user_ids_vocabulary.vocab_size(), 64)])movie_model = tf.keras.Sequential([    movie_titles_vocabulary,    tf.keras.layers.Embedding(movie_titles_vocabulary.vocab_size(), 64)])# Define your objectives.task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(    movies.batch(128).map(movie_model)  ))

Fit and evaluate it.

Create the model, train it, and generate predictions:

# Create a retrieval model.model = MovieLensModel(user_model, movie_model, task)model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))# Train for 3 epochs.model.fit(ratings.batch(4096), epochs=3)# Use brute-force search to set up retrieval using the trained representations.index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)index.index(movies.batch(100).map(model.movie_model), movies)# Get some recommendations._, titles = index(np.array(["42"]))print(f"Top 3 recommendations for user 42: {titles[0, :3]}")

代码地址: https://codechina.csdn.net/cs...