turicreate.recommender.ranking_factorization_recommender.RankingFactorizationRecommender.predict¶

RankingFactorizationRecommender.predict(dataset, new_observation_data=None, new_user_data=None, new_item_data=None)¶

Return a score prediction for the user ids and item ids in the provided data set.

Parameters:

dataset : SFrame: Dataset in the same form used for training.
new_observation_data : SFrame, optional: new_observation_data gives additional observation data to the model, which may be used by the models to improve score accuracy. Must be in the same format as the observation data passed to create. How this data is used varies by model.
new_user_data : SFrame, optional: new_user_data may give additional user data to the model. If present, scoring is done with reference to this new information. If there is any overlap with the side information present at training time, then this new side data is preferred. Must be in the same format as the user data passed to create.
new_item_data : SFrame, optional: new_item_data may give additional item data to the model. If present, scoring is done with reference to this new information. If there is any overlap with the side information present at training time, then this new side data is preferred. Must be in the same format as the item data passed to create.

Returns:

out : SArray: An SArray with predicted scores for each given observation predicted by the model.