https://github.com/tensorflow/recommenders

TensorFlow Recommenders is a library for building recommender system models using TensorFlow.
https://github.com/tensorflow/recommenders

recommender recommender-system tensorflow tensorflow-recommenders

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TensorFlow Recommenders is a library for building recommender system models using TensorFlow.

• Host: GitHub
• URL: https://github.com/tensorflow/recommenders
• Owner: tensorflow
• License: apache-2.0
• Created: 2020-06-26T21:38:01.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-12-05T06:01:33.000Z (about 1 month ago)
• Last Synced: 2024-12-31T04:03:12.152Z (12 days ago)
• Topics: recommender, recommender-system, tensorflow, tensorflow-recommenders
• Language: Python
• Homepage:
• Size: 1.87 MB
• Stars: 1,872
• Watchers: 50
• Forks: 281
• Open Issues: 270
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Citation: CITATION.cff

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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)

```