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, Text
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import 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]}")


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