Active Learning for Recommender Systems

Excerpt

Recommender systems learn user preferences and provide them personalized recommendations. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new-user problem. A simple and effective way to overcome this problem is posing queries to new users so that they express their preferences about selected items, e.g., by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e., ratings from users that will help most in determining their interests. Active learning is a learning algorithm that is able to interactively query the Oracle to obtain labels for data instances. The Oracle is a user or teacher who knows the labels. …