TensorFlow introduces ‘TensorFlow Recommenders’ (TFRS)

This is big news for the recommender-system community: Maciej Kula and James Chen from Google Brain announce TensorFlow Recommenders (TFRS), an official recommender-systems package for TensorFlow, the major deep-learning library.

Today, we’re excited to introduce TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy. Built with TensorFlow 2.x, TFRS makes it possible to:

* Build and evaluate flexible candidate nomination models;

* Freely incorporate item, user, and context information into recommendation models;

* Train multi-task models that jointly optimize multiple recommendation objectives;

* Efficiently serve the resulting models using TensorFlow Serving.

TFRS is based on TensorFlow 2.x and Keras, making it instantly familiar and user-friendly. It is modular by design (so that you can easily customize individual layers and metrics), but still forms a cohesive whole (so that the individual components work well together). Throughout the design of TFRS, we’ve emphasized flexibility and ease-of-use: default settings should be sensible; common tasks should be intuitive and straightforward to implement; more complex or custom recommendation tasks should be possible.


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