TensorFlow Recommenders
An open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy.
recommendation-systems tensorflow article code library

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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