Keras Recommenders is a library for building recommender systems on top of Keras 3. Keras Recommenders works natively with TensorFlow, JAX, or PyTorch. It provides a collection of building blocks which help with the full workflow of creating a recommender system. As it's built on Keras 3, models can be trained and serialized in any framework and re-used in another without costly migrations.
This library is an extension of the core Keras API; all high-level modules receive that same level of polish as core Keras. If you are familiar with Keras, congratulations! You already understand most of Keras Recommenders.
If you have Keras 3 installed in your environment (see installation above), you
can use Keras Recommenders with any of JAX, TensorFlow and PyTorch. To do so,
set the KERAS_BACKEND environment variable. For example:
export KERAS_BACKEND=jaxOr in Colab, with:
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_recommendersImportant
Make sure to set the KERAS_BACKEND before importing any Keras libraries;
it will be used to set up Keras when it is first imported.
We follow Semantic Versioning, and plan to provide
backwards compatibility guarantees both for code and saved models built with our
components. While we continue with pre-release 0.y.z development, we may break
compatibility at any time and APIs should not be considered stable.
If Keras Recommenders helps your research, we appreciate your citations. Here is the BibTeX entry:
@misc{kerasrecommenders2024,
title={KerasRecommenders},
author={Hertschuh, Fabien and Chollet, Fran\c{c}ois and others},
year={2024},
howpublished={\url{https://github.com/keras-team/keras-rs}},
}Thank you to all of our wonderful contributors!