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.