# recommenders

**Repository Path**: mirrors_tensorflow/recommenders

## Basic Information

- **Project Name**: recommenders
- **Description**: TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-18
- **Last Updated**: 2026-02-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TensorFlow Recommenders

![TensorFlow Recommenders logo](assets/full_logo.png)

![TensorFlow Recommenders build badge](https://github.com/tensorflow/recommenders/actions/workflows/test.yaml/badge.svg)
[![PyPI badge](https://img.shields.io/pypi/v/tensorflow-recommenders.svg)](https://pypi.python.org/pypi/tensorflow-recommenders/)

TensorFlow Recommenders is a library for building recommender system models
using [TensorFlow](https://www.tensorflow.org).

It helps with the full workflow of building a recommender system: data
preparation, model formulation, training, evaluation, and deployment.

It's built on Keras and aims to have a gentle learning curve while still giving
you the flexibility to build complex models.

## Installation

Make sure you have TensorFlow 2.x installed, and install from `pip`:

```shell
pip install tensorflow-recommenders
```

## Documentation

Have a look at our
[tutorials](https://tensorflow.org/recommenders/examples/quickstart) and
[API reference](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/).

## Quick start

Building a factorization model for the Movielens 100K dataset is very simple
([Colab](https://tensorflow.org/recommenders/examples/quickstart)):

```python
from typing import Dict, Text

import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs

# Ratings data.
ratings = tfds.load('movielens/100k-ratings', split="train")
# Features of all the available movies.
movies = tfds.load('movielens/100k-movies', split="train")

# Select the basic features.
ratings = ratings.map(lambda x: {
    "movie_id": tf.strings.to_number(x["movie_id"]),
    "user_id": tf.strings.to_number(x["user_id"])
})
movies = movies.map(lambda x: tf.strings.to_number(x["movie_id"]))

# Build a model.
class Model(tfrs.Model):

  def __init__(self):
    super().__init__()

    # Set up user representation.
    self.user_model = tf.keras.layers.Embedding(
        input_dim=2000, output_dim=64)
    # Set up movie representation.
    self.item_model = tf.keras.layers.Embedding(
        input_dim=2000, output_dim=64)
    # Set up a retrieval task and evaluation metrics over the
    # entire dataset of candidates.
    self.task = tfrs.tasks.Retrieval(
        metrics=tfrs.metrics.FactorizedTopK(
            candidates=movies.batch(128).map(self.item_model)
        )
    )

  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:

    user_embeddings = self.user_model(features["user_id"])
    movie_embeddings = self.item_model(features["movie_id"])

    return self.task(user_embeddings, movie_embeddings)


model = Model()
model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))

# Randomly shuffle data and split between train and test.
tf.random.set_seed(42)
shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)

train = shuffled.take(80_000)
test = shuffled.skip(80_000).take(20_000)

# Train.
model.fit(train.batch(4096), epochs=5)

# Evaluate.
model.evaluate(test.batch(4096), return_dict=True)
```