recommender — Turi Create API 6.4.1 documentation (original) (raw)

Turi Create API

The Turi Create recommender toolkit provides a unified interface to train a variety of recommender models and use them to make recommendations.

Recommender models can be created using turicreate.recommender.create() or loaded from a previously saved model using turicreate.load_model(). The input data must be an SFrame with a column containing user ids, a column containing item ids, and optionally a column containing target values such as movie ratings, etc. When a target is not provided (as is the case in implicit feedback settings), then a collaborative filtering model based on item-item similarity is returned. For more details, please see the documentation forturicreate.recommender.create().

A recommender model object can perform key tasks includingpredict, recommend, evaluate, and save. Model attributes and statistics may be obtained via m.get(), where m is a model object. In particular, trained model parameters may be accessed usingm.get(‘coefficients’) or equivalently m[‘coefficients’]. For more details, please see individual model API documentation below.

In addition to the API documentation, please see the recommender systems chapter of the User Guidefor more details and extended examples.

sf = turicreate.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"], ... 'item_id': ["a", "b", "c", "a", "b", "b", "c", "d"], ... 'rating': [1, 3, 2, 5, 4, 1, 4, 3]}) m = turicreate.recommender.create(sf, target='rating') recs = m.recommend() print recs +---------+---------+---------------+------+ | user_id | item_id | score | rank | +---------+---------+---------------+------+ | 0 | d | 2.42301885789 | 1 | | 1 | c | 5.52301720893 | 1 | | 1 | d | 5.20882169849 | 2 | | 2 | a | 2.149379798 | 1 | +---------+---------+---------------+------+ [4 rows x 4 columns]

m['coefficients'] {'intercept': 3.1321961361684068, 'item_id': Columns: item_id str linear_terms float factors array

Rows: 4

Data: +---------+-----------------+--------------------------------+ | item_id | linear_terms | factors | +---------+-----------------+--------------------------------+ | a | -0.381912890376 | array('d', [0.006779233276 ... | | b | -0.482275197699 | array('d', [-3.57188659440 ... | | c | 0.664901063905 | array('d', [-0.00025265078 ... | | d | 0.352987048665 | array('d', [-0.00197509767 ... | +---------+-----------------+--------------------------------+ [4 rows x 3 columns], 'user_id': Columns: user_id str linear_terms float factors array

Rows: 3

Data: +---------+-----------------+--------------------------------+ | user_id | linear_terms | factors | +---------+-----------------+--------------------------------+ | 0 | -1.06188402031 | array('d', [-0.00321943390 ... | | 1 | 1.72356956865 | array('d', [0.005337682218 ... | | 2 | -0.604970370745 | array('d', [-0.00274082382 ... | +---------+-----------------+--------------------------------+ [3 rows x 3 columns]}

creating a recommender

create A unified interface for training recommender models.

item content recommenders

item_content_recommender.create Create a content-based recommender model in which the similarity between the items recommended is determined by the content of those items rather than learned from user interaction data.
item_content_recommender.ItemContentRecommender A recommender based on the similarity between item content rather using user interaction patterns to compute similarity.