Keras Recommenders

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.
Keras Recommenders is available on PyPI as keras-rs:
To try out the latest version of Keras Recommenders, you can use our nightly package:
Read Getting started with Keras for more information on installing Keras 3 and compatibility with different frameworks.
Import KerasRS and other libraries:
Define a simple model using the FeatureCross layer:
Call model.fit() on dummy data:
If your task is to rank items in a list, you can make use of the ranking losses and metrics which KerasRS provides. Below, we use the pairwise hinge loss and track the nDCG metric:
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:
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: