|---|---|---|---| | [View on TensorFlow.org](https://www.tensorflow.org/recommenders/quickstart) | [Run in Google Colab](https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/quickstart.ipynb) | [View source on GitHub](https://github.com/tensorflow/recommenders/blob/main/docs/examples/quickstart.ipynb) | [Download notebook](https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/quickstart.ipynb) | In this tutorial, we build a simple matrix factorization model using the [MovieLens 100K dataset](https://grouplens.org/datasets/movielens/100k/) with TFRS. We can use this model to recommend movies for a given user. ### Import TFRS First, install and import TFRS: pip install -q tensorflow-recommenders pip install -q --upgrade tensorflow-datasets from typing import Dict, Text import numpy as np import tensorflow as tf import tensorflow_datasets as tfds import tensorflow_recommenders as tfrs ### Read the data # 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_title": x["movie_title"], "user_id": x["user_id"] }) movies = movies.map(lambda x: x["movie_title"]) Build vocabularies to convert user ids and movie titles into integer indices for embedding layers: user_ids_vocabulary = tf.keras.layers.StringLookup(mask_token=None) user_ids_vocabulary.adapt(ratings.map(lambda x: x["user_id"])) movie_titles_vocabulary = tf.keras.layers.StringLookup(mask_token=None) movie_titles_vocabulary.adapt(movies) ### Define a model We can define a TFRS model by inheriting from [`tfrs.Model`](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/models/Model) and implementing the `compute_loss` method: class MovieLensModel(tfrs.Model): # We derive from a custom base class to help reduce boilerplate. Under the hood, # these are still plain Keras Models. def __init__( self, user_model: tf.keras.Model, movie_model: tf.keras.Model, task: tfrs.tasks.Retrieval): super().__init__() # Set up user and movie representations. self.user_model = user_model self.movie_model = movie_model # Set up a retrieval task. self.task = task def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor: # Define how the loss is computed. user_embeddings = self.user_model(features["user_id"]) movie_embeddings = self.movie_model(features["movie_title"]) return self.task(user_embeddings, movie_embeddings) Define the two models and the retrieval task. # Define user and movie models. user_model = tf.keras.Sequential([ user_ids_vocabulary, tf.keras.layers.Embedding(user_ids_vocabulary.vocab_size(), 64) ]) movie_model = tf.keras.Sequential([ movie_titles_vocabulary, tf.keras.layers.Embedding(movie_titles_vocabulary.vocab_size(), 64) ]) # Define your objectives. task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK( movies.batch(128).map(movie_model) ) ) ``` WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. ``` ### Fit and evaluate it. Create the model, train it, and generate predictions: # Create a retrieval model. model = MovieLensModel(user_model, movie_model, task) model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5)) # Train for 3 epochs. model.fit(ratings.batch(4096), epochs=3) # Use brute-force search to set up retrieval using the trained representations. index = tfrs.layers.factorized_top_k.BruteForce(model.user_model) index.index_from_dataset( movies.batch(100).map(lambda title: (title, model.movie_model(title)))) # Get some recommendations. _, titles = index(np.array(["42"])) print(f"Top 3 recommendations for user 42: {titles[0, :3]}") ``` Epoch 1/3 25/25 [==============================] - 8s 255ms/step - factorized_top_k/top_1_categorical_accuracy: 7.0000e-05 - factorized_top_k/top_5_categorical_accuracy: 0.0016 - factorized_top_k/top_10_categorical_accuracy: 0.0051 - factorized_top_k/top_50_categorical_accuracy: 0.0442 - factorized_top_k/top_100_categorical_accuracy: 0.1000 - loss: 33087.9238 - regularization_loss: 0.0000e+00 - total_loss: 33087.9238 Epoch 2/3 25/25 [==============================] - 6s 244ms/step - factorized_top_k/top_1_categorical_accuracy: 1.6000e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0045 - factorized_top_k/top_10_categorical_accuracy: 0.0145 - factorized_top_k/top_50_categorical_accuracy: 0.1054 - factorized_top_k/top_100_categorical_accuracy: 0.2106 - loss: 31014.9355 - regularization_loss: 0.0000e+00 - total_loss: 31014.9355 Epoch 3/3 25/25 [==============================] - 6s 245ms/step - factorized_top_k/top_1_categorical_accuracy: 3.8000e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0078 - factorized_top_k/top_10_categorical_accuracy: 0.0213 - factorized_top_k/top_50_categorical_accuracy: 0.1438 - factorized_top_k/top_100_categorical_accuracy: 0.2673 - loss: 30424.1900 - regularization_loss: 0.0000e+00 - total_loss: 30424.1900 Top 3 recommendations for user 42: [b'Rent-a-Kid (1995)' b'Land Before Time III: The Time of the Great Giving (1995) (V)' b'House Arrest (1996)'] ```