tensorrec_getting_started_prediction.py

# Pull user 432's features out of the user features matrix and predict movie ranks for just that user
u432_features = sparse.csr_matrix(user_indicator_features)[432]
u432_rankings = hybrid_model.predict_rank(user_features=u432_features,
                                          item_features=full_item_features)[0]

# Get internal IDs of User 432's top 10 recommendations
# These are sorted by item ID, not by rank
# This may contain items with which User 432 has already interacted
u432_top_ten_recs = numpy.where(u432_rankings <= 10)[0]
print("User 432 recommendations:")
for m in u432_top_ten_recs:
    print(movie_titles_by_internal_id[m])