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TensorFlow Recommenders

import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs

# Load data on movie ratings.
ratings = tfds.load("movielens/100k-ratings", split="train")
movies = tfds.load("movielens/100k-movies", split="train")

# Build flexible representation models.
user_model = tf.keras.Sequential([...])
movie_model = tf.keras.Sequential([...])

# Define your objectives.
task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(

# Create a retrieval model.
model = MovielensModel(user_model, movie_model, task)

# Train., epochs=3)

# Set up retrieval using trained representations.
index = tfrs.layers.ann.BruteForce(model.user_model)
index.index(movies.batch(100).map(model.movie_model), movies)

# Get recommendations.
_, titles = index(np.array(["42"]))
print(f"Recommendations for user 42: {titles[0, :3]}")
Run in a Notebook
TensorFlow Recommenders (TFRS) is a library for building recommender system models.

It helps with the full workflow of building a recommender system: data preparation, model formulation, training, evaluation, and deployment.

It's built on Keras and aims to have a gentle learning curve while still giving you the flexibility to build complex models.

TFRS makes it possible to:
  • Build and evaluate flexible recommendation retrieval models.
  • Freely incorporate item, user, and context information into recommendation models.
  • Train multi-task models that jointly optimize multiple recommendation objectives.
TFRS is open source and available on Github.

To learn more, see the tutorial on how to build a movie recommender system, or check the API docs for the API reference.