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form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► <a href='/examples/'>Code examples</a> / <a href='/examples/structured_data/'>Structured Data</a> / Collaborative Filtering for Movie Recommendations </div> <div class='k-content'> <h1 id="collaborative-filtering-for-movie-recommendations">Collaborative Filtering for Movie Recommendations</h1> Author: <a href="https://twitter.com/sidd2006">Siddhartha Banerjee</a> Date created: 2020/05/24 Last modified: 2020/05/24 Description: Recommending movies using a model trained on Movielens dataset. <div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div> <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/structured_data/ipynb/collaborative_filtering_movielens.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/examples/structured_data/collaborative_filtering_movielens.py">GitHub source</a> <hr /> <h2 id="introduction">Introduction</h2> This example demonstrates <a href="https://en.wikipedia.org/wiki/Collaborative_filtering">Collaborative filtering</a> using the <a href="https://www.kaggle.com/c/movielens-100k">Movielens dataset</a> to recommend movies to users. The MovieLens ratings dataset lists the ratings given by a set of users to a set of movies. Our goal is to be able to predict ratings for movies a user has not yet watched. The movies with the highest predicted ratings can then be recommended to the user. The steps in the model are as follows: <ol> <li>Map user ID to a "user vector" via an embedding matrix</li> <li>Map movie ID to a "movie vector" via an embedding matrix</li> <li>Compute the dot product between the user vector and movie vector, to obtain the a match score between the user and the movie (predicted rating).</li> <li>Train the embeddings via gradient descent using all known user-movie pairs.</li> </ol> References: <ul> <li><a href="https://dl.acm.org/doi/pdf/10.1145/371920.372071">Collaborative Filtering</a></li> <li><a href="https://dl.acm.org/doi/pdf/10.1145/3038912.3052569">Neural Collaborative Filtering</a></li> </ul> <div class="codehilite"><pre><code>import pandas as pd from pathlib import Path import matplotlib.pyplot as plt import numpy as np from zipfile import ZipFile import keras from keras import layers from keras import ops </code></pre></div> <hr /> <h2 id="first-load-the-data-and-apply-preprocessing">First, load the data and apply preprocessing</h2> <div class="codehilite"><pre><code># Download the actual data from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip" # Use the ratings.csv file movielens_data_file_url = ( "http://files.grouplens.org/datasets/movielens/ml-latest-small.zip" ) movielens_zipped_file = keras.utils.get_file( "ml-latest-small.zip", movielens_data_file_url, extract=False ) keras_datasets_path = Path(movielens_zipped_file).parents[0] movielens_dir = keras_datasets_path / "ml-latest-small" # Only extract the data the first time the script is run. if not movielens_dir.exists(): with ZipFile(movielens_zipped_file, "r") as zip: # Extract files print("Extracting all the files now...") zip.extractall(path=keras_datasets_path) print("Done!") ratings_file = movielens_dir / "ratings.csv" df = pd.read_csv(ratings_file) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Downloading data from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip 978202/978202 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step Extracting all the files now... Done! </code></pre></div> </div> First, need to perform some preprocessing to encode users and movies as integer indices. <div class="codehilite"><pre><code>user_ids = df["userId"].unique().tolist() user2user_encoded = {x: i for i, x in enumerate(user_ids)} userencoded2user = {i: x for i, x in enumerate(user_ids)} movie_ids = df["movieId"].unique().tolist() movie2movie_encoded = {x: i for i, x in enumerate(movie_ids)} movie_encoded2movie = {i: x for i, x in enumerate(movie_ids)} df["user"] = df["userId"].map(user2user_encoded) df["movie"] = df["movieId"].map(movie2movie_encoded) num_users = len(user2user_encoded) num_movies = len(movie_encoded2movie) df["rating"] = df["rating"].values.astype(np.float32) # min and max ratings will be used to normalize the ratings later min_rating = min(df["rating"]) max_rating = max(df["rating"]) print( "Number of users: {}, Number of Movies: {}, Min rating: {}, Max rating: {}".format( num_users, num_movies, min_rating, max_rating ) ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Number of users: 610, Number of Movies: 9724, Min rating: 0.5, Max rating: 5.0 </code></pre></div> </div> <hr /> <h2 id="prepare-training-and-validation-data">Prepare training and validation data</h2> <div class="codehilite"><pre><code>df = df.sample(frac=1, random_state=42) x = df[["user", "movie"]].values # Normalize the targets between 0 and 1. Makes it easy to train. y = df["rating"].apply(lambda x: (x - min_rating) / (max_rating - min_rating)).values # Assuming training on 90% of the data and validating on 10%. train_indices = int(0.9 * df.shape[0]) x_train, x_val, y_train, y_val = ( x[:train_indices], x[train_indices:], y[:train_indices], y[train_indices:], ) </code></pre></div> <hr /> <h2 id="create-the-model">Create the model</h2> We embed both users and movies in to 50-dimensional vectors. The model computes a match score between user and movie embeddings via a dot product, and adds a per-movie and per-user bias. The match score is scaled to the <code>[0, 1]</code> interval via a sigmoid (since our ratings are normalized to this range). <div class="codehilite"><pre><code>EMBEDDING_SIZE = 50 class RecommenderNet(keras.Model): def __init__(self, num_users, num_movies, embedding_size, **kwargs): super().__init__(**kwargs) self.num_users = num_users self.num_movies = num_movies self.embedding_size = embedding_size self.user_embedding = layers.Embedding( num_users, embedding_size, embeddings_initializer="he_normal", embeddings_regularizer=keras.regularizers.l2(1e-6), ) self.user_bias = layers.Embedding(num_users, 1) self.movie_embedding = layers.Embedding( num_movies, embedding_size, embeddings_initializer="he_normal", embeddings_regularizer=keras.regularizers.l2(1e-6), ) self.movie_bias = layers.Embedding(num_movies, 1) def call(self, inputs): user_vector = self.user_embedding(inputs[:, 0]) user_bias = self.user_bias(inputs[:, 0]) movie_vector = self.movie_embedding(inputs[:, 1]) movie_bias = self.movie_bias(inputs[:, 1]) dot_user_movie = ops.tensordot(user_vector, movie_vector, 2) # Add all the components (including bias) x = dot_user_movie + user_bias + movie_bias # The sigmoid activation forces the rating to between 0 and 1 return ops.nn.sigmoid(x) model = RecommenderNet(num_users, num_movies, EMBEDDING_SIZE) model.compile( loss=keras.losses.BinaryCrossentropy(), optimizer=keras.optimizers.Adam(learning_rate=0.001), ) </code></pre></div> <hr /> <h2 id="train-the-model-based-on-the-data-split">Train the model based on the data split</h2> <div class="codehilite"><pre><code>history = model.fit( x=x_train, y=y_train, batch_size=64, epochs=5, verbose=1, validation_data=(x_val, y_val), ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1/5 1418/1418 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - loss: 0.6591 - val_loss: 0.6201 Epoch 2/5 1418/1418 ━━━━━━━━━━━━━━━━━━━━ 1s 894us/step - loss: 0.6159 - val_loss: 0.6191 Epoch 3/5 1418/1418 ━━━━━━━━━━━━━━━━━━━━ 1s 977us/step - loss: 0.6093 - val_loss: 0.6138 Epoch 4/5 1418/1418 ━━━━━━━━━━━━━━━━━━━━ 1s 865us/step - loss: 0.6100 - val_loss: 0.6123 Epoch 5/5 1418/1418 ━━━━━━━━━━━━━━━━━━━━ 1s 854us/step - loss: 0.6072 - val_loss: 0.6121 </code></pre></div> </div> <hr /> <h2 id="plot-training-and-validation-loss">Plot training and validation loss</h2> <div class="codehilite"><pre><code>plt.plot(history.history["loss"]) plt.plot(history.history["val_loss"]) plt.title("model loss") plt.ylabel("loss") plt.xlabel("epoch") plt.legend(["train", "test"], loc="upper left") plt.show() </code></pre></div> <img alt="png" src="/img/examples/structured_data/collaborative_filtering_movielens/collaborative_filtering_movielens_14_0.png" /> <hr /> <h2 id="show-top-10-movie-recommendations-to-a-user">Show top 10 movie recommendations to a user</h2> <div class="codehilite"><pre><code>movie_df = pd.read_csv(movielens_dir / "movies.csv") # Let us get a user and see the top recommendations. user_id = df.userId.sample(1).iloc[0] movies_watched_by_user = df[df.userId == user_id] movies_not_watched = movie_df[ ~movie_df["movieId"].isin(movies_watched_by_user.movieId.values) ]["movieId"] movies_not_watched = list( set(movies_not_watched).intersection(set(movie2movie_encoded.keys())) ) movies_not_watched = [[movie2movie_encoded.get(x)] for x in movies_not_watched] user_encoder = user2user_encoded.get(user_id) user_movie_array = np.hstack( ([[user_encoder]] * len(movies_not_watched), movies_not_watched) ) ratings = model.predict(user_movie_array).flatten() top_ratings_indices = ratings.argsort()[-10:][::-1] recommended_movie_ids = [ movie_encoded2movie.get(movies_not_watched[x][0]) for x in top_ratings_indices ] print("Showing recommendations for user: {}".format(user_id)) print("====" * 9) print("Movies with high ratings from user") print("----" * 8) top_movies_user = ( movies_watched_by_user.sort_values(by="rating", ascending=False) .head(5) .movieId.values ) movie_df_rows = movie_df[movie_df["movieId"].isin(top_movies_user)] for row in movie_df_rows.itertuples(): print(row.title, ":", row.genres) print("----" * 8) print("Top 10 movie recommendations") print("----" * 8) recommended_movies = movie_df[movie_df["movieId"].isin(recommended_movie_ids)] for row in recommended_movies.itertuples(): print(row.title, ":", row.genres) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code> 272/272 ━━━━━━━━━━━━━━━━━━━━ 0s 714us/step Showing recommendations for user: 249 ==================================== Movies with high ratings from user -------------------------------- Fight Club (1999) : Action|Crime|Drama|Thriller Serenity (2005) : Action|Adventure|Sci-Fi Departed, The (2006) : Crime|Drama|Thriller Prisoners (2013) : Drama|Mystery|Thriller Arrival (2016) : Sci-Fi -------------------------------- Top 10 movie recommendations -------------------------------- In the Name of the Father (1993) : Drama Monty Python and the Holy Grail (1975) : Adventure|Comedy|Fantasy Princess Bride, The (1987) : Action|Adventure|Comedy|Fantasy|Romance Lawrence of Arabia (1962) : Adventure|Drama|War Apocalypse Now (1979) : Action|Drama|War Full Metal Jacket (1987) : Drama|War Amadeus (1984) : Drama Glory (1989) : Drama|War Chinatown (1974) : Crime|Film-Noir|Mystery|Thriller City of God (Cidade de Deus) (2002) : Action|Adventure|Crime|Drama|Thriller </code></pre></div> </div> </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#collaborative-filtering-for-movie-recommendations'>Collaborative Filtering for Movie Recommendations</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#introduction'>Introduction</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#first-load-the-data-and-apply-preprocessing'>First, load the data and apply preprocessing</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#prepare-training-and-validation-data'>Prepare training and validation data</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#create-the-model'>Create the model</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#train-the-model-based-on-the-data-split'>Train the model based on the data split</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#plot-training-and-validation-loss'>Plot training and validation loss</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#show-top-10-movie-recommendations-to-a-user'>Show top 10 movie recommendations to a user</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>