class turicreate.recommender.item_content_recommender.ItemContentRecommender(model_proxy)

A recommender based on the similarity between item content rather using user interaction patterns to compute similarity.

Creating an ItemContentRecommender

This model cannot be constructed directly. Instead, use turicreate.recommender.item_content_recommender.create() to create an instance of this model. A detailed list of parameter options and code samples are available in the documentation for the create function.


Model Definition

This model first computes the similarity between items using the content of each item. The similarity score between two items is calculated by first computing the similarity between the item data for each column, then taking a weighted average of the per-column similarities to get the final similarity. The recommendations are generated according to the average similarity of a candidate item to all the items in a user’s set of rated items.

For more examples, see the associated create function.


ItemContentRecommender.evaluate(dataset[, …]) Evaluate the model’s ability to make rating predictions or recommendations.
ItemContentRecommender.evaluate_precision_recall(dataset) Compute a model’s precision and recall scores for a particular dataset.
ItemContentRecommender.evaluate_rmse(…) Evaluate the prediction error for each user-item pair in the given data set.
ItemContentRecommender.export_coreml(filename) Export the model in Core ML format.
ItemContentRecommender.get_num_items_per_user() Get the number of items observed for each user.
ItemContentRecommender.get_num_users_per_item() Get the number of users observed for each item.
ItemContentRecommender.get_similar_items([…]) Get the k most similar items for each item in items.
ItemContentRecommender.get_similar_users([…]) Get the k most similar users for each entry in users.
ItemContentRecommender.predict(dataset[, …]) Return a score prediction for the user ids and item ids in the provided data set.
ItemContentRecommender.recommend([users, k, …]) Recommend the k highest scored items for each user.
ItemContentRecommender.recommend_from_interactions(…) Recommend the k highest scored items based on the
ItemContentRecommender.save(location) Save the model.
ItemContentRecommender.summary([output]) Print a summary of the model.