tensorrec_getting_started_contentbased.py

# Fit a content-based model using the genres as item features
print("Training content-based recommender")
content_model = tensorrec.TensorRec(
    n_components=n_genres,
    item_repr_graph=tensorrec.representation_graphs.FeaturePassThroughRepresentationGraph(),
    loss_graph=tensorrec.loss_graphs.WMRBLossGraph()
)
content_model.fit(interactions=sparse_train_ratings_4plus,
                  user_features=user_indicator_features,
                  item_features=movie_genre_features,
                  n_sampled_items=int(n_items * .01))

# Check the results of the content-based model
print("Content-based recommender:")
predicted_ranks = content_model.predict_rank(user_features=user_indicator_features,
                                             item_features=movie_genre_features)
check_results(predicted_ranks)