tensorrec_getting_started_hybrid.py

# Try concatenating the genres on to the indicator features for a hybrid recommender system
full_item_features = sparse.hstack([item_indicator_features, movie_genre_features])

print("Training hybrid recommender")
hybrid_model = tensorrec.TensorRec(
    n_components=5,
    loss_graph=tensorrec.loss_graphs.WMRBLossGraph()
)
hybrid_model.fit(interactions=sparse_train_ratings_4plus,
                 user_features=user_indicator_features,
                 item_features=full_item_features,
                 n_sampled_items=int(n_items * .01))

print("Hybrid recommender:")
predicted_ranks = hybrid_model.predict_rank(user_features=user_indicator_features,
                                            item_features=full_item_features)
check_results(predicted_ranks)