# TensorFlow Recommenders
[](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)
```
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"description": "# TensorFlow Recommenders\n\n\n\n\n[](https://pypi.python.org/pypi/tensorflow-recommenders/)\n\nTensorFlow Recommenders is a library for building recommender system models\nusing [TensorFlow](https://www.tensorflow.org).\n\nIt helps with the full workflow of building a recommender system: data\npreparation, model formulation, training, evaluation, and deployment.\n\nIt's built on Keras and aims to have a gentle learning curve while still giving\nyou the flexibility to build complex models.\n\n## Installation\n\nMake sure you have TensorFlow 2.x installed, and install from `pip`:\n\n```shell\npip install tensorflow-recommenders\n```\n\n## Documentation\n\nHave a look at our\n[tutorials](https://tensorflow.org/recommenders/examples/quickstart) and\n[API reference](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/).\n\n## Quick start\n\nBuilding a factorization model for the Movielens 100K dataset is very simple\n([Colab](https://tensorflow.org/recommenders/examples/quickstart)):\n\n```python\nfrom typing import Dict, Text\n\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\nimport tensorflow_recommenders as tfrs\n\n# Ratings data.\nratings = tfds.load('movielens/100k-ratings', split=\"train\")\n# Features of all the available movies.\nmovies = tfds.load('movielens/100k-movies', split=\"train\")\n\n# Select the basic features.\nratings = ratings.map(lambda x: {\n \"movie_id\": tf.strings.to_number(x[\"movie_id\"]),\n \"user_id\": tf.strings.to_number(x[\"user_id\"])\n})\nmovies = movies.map(lambda x: tf.strings.to_number(x[\"movie_id\"]))\n\n# Build a model.\nclass Model(tfrs.Model):\n\n def __init__(self):\n super().__init__()\n\n # Set up user representation.\n self.user_model = tf.keras.layers.Embedding(\n input_dim=2000, output_dim=64)\n # Set up movie representation.\n self.item_model = tf.keras.layers.Embedding(\n input_dim=2000, output_dim=64)\n # Set up a retrieval task and evaluation metrics over the\n # entire dataset of candidates.\n self.task = tfrs.tasks.Retrieval(\n metrics=tfrs.metrics.FactorizedTopK(\n candidates=movies.batch(128).map(self.item_model)\n )\n )\n\n def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n\n user_embeddings = self.user_model(features[\"user_id\"])\n movie_embeddings = self.item_model(features[\"movie_id\"])\n\n return self.task(user_embeddings, movie_embeddings)\n\n\nmodel = Model()\nmodel.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))\n\n# Randomly shuffle data and split between train and test.\ntf.random.set_seed(42)\nshuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)\n\ntrain = shuffled.take(80_000)\ntest = shuffled.skip(80_000).take(20_000)\n\n# Train.\nmodel.fit(train.batch(4096), epochs=5)\n\n# Evaluate.\nmodel.evaluate(test.batch(4096), return_dict=True)\n```\n\n\n",
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