tensorrec_getting_started_mf_cf.py

# Construct indicator features for users and items
user_indicator_features = sparse.identity(n_users)
item_indicator_features = sparse.identity(n_items)

# Build a matrix factorization collaborative filter model
cf_model = tensorrec.TensorRec(n_components=5)

# Fit the collaborative filter model
print("Training collaborative filter")
cf_model.fit(interactions=sparse_train_ratings,
             user_features=user_indicator_features,
             item_features=item_indicator_features)