This release is a pre-release and may not be stable for production use.
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.
Quick Links
Quickstart
Train your own cross network
Choose a backend:
import os
os.environ["KERAS_BACKEND"] = "jax" # Or "tensorflow" or "torch"!
Import KerasRS and other libraries:
import keras
import keras_rs
import numpy as np
Define a simple model using the FeatureCross layer:
vocabulary_size = 32
embedding_dim = 6
inputs = keras.Input(shape=(), name='indices', dtype="int32")
x0 = keras.layers.Embedding(
input_dim=vocabulary_size,
output_dim=embedding_dim
)(inputs)
x1 = keras_rs.layers.FeatureCross()(x0, x0)
x2 = keras_rs.layers.FeatureCross()(x0, x1)
output = keras.layers.Dense(units=10)(x2)
model = keras.Model(inputs, output)
Compile the model:
model.compile(
loss=keras.losses.MeanSquaredError(),
optimizer=keras.optimizers.Adam(learning_rate=3e-4)
)
Call model.fit() on dummy data:
batch_size = 2
x = np.random.randint(0, vocabulary_size, size=(batch_size,))
y = np.random.random(size=(batch_size,))
model.fit(x, y=y)
Use ranking losses and metrics
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:
model.compile(
loss=keras_rs.losses.PairwiseHingeLoss(),
metrics=[keras_rs.metrics.NDCG()],
optimizer=keras.optimizers.Adam(learning_rate=3e-4),
)
Installation
Keras Recommenders is available on PyPI as keras-rs:
pip install keras-rs
To try out the latest version of Keras Recommenders, you can use our nightly package:
pip install keras-rs-nightly
Read Getting started with Keras for more information on installing Keras 3 and compatibility with different frameworks.
[!IMPORTANT] We recommend using Keras Recommenders with TensorFlow 2.16 or later, as TF 2.16 packages Keras 3 by default.
Configuring your backend
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=jax
Or in Colab, with:
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_rs
[!IMPORTANT] Make sure to set the
KERAS_BACKENDbefore importing any Keras libraries; it will be used to set up Keras when it is first imported.
Compatibility
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.
Citing Keras Recommenders
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 Sharma, Abheesht and others},
year={2024},
howpublished={\url{https://github.com/keras-team/keras-rs}},
}
Acknowledgements
Thank you to all of our wonderful contributors!
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