# Let's try a new loss function: WMRB
print("Training collaborative filter with WMRB loss")
ranking_cf_model = tensorrec.TensorRec(n_components=5,
                                       loss_graph=tensorrec.loss_graphs.WMRBLossGraph())
ranking_cf_model.fit(interactions=sparse_train_ratings_4plus,
                     user_features=user_indicator_features,
                     item_features=item_indicator_features,
                     n_sampled_items=int(n_items * .01))

# Check the results of the WMRB MF CF model
print("WMRB matrix factorization collaborative filter:")
predicted_ranks = ranking_cf_model.predict_rank(user_features=user_indicator_features,
                                                item_features=item_indicator_features)