Active Learning for Co-Clustering Based Collaborative Filtering

This paper proposes combining expected value of rating information with likelihood of getting ratings from the users to form the sample selection criterion and shows that the proposed method outperforms three popular active learning strategies for collaborative filtering.

Abstract

Collaborative filtering, a technique for making predictions about user preferences by exploiting behavior patterns of groups of users, has become a main prediction technique in recommender systems. One crucial problem for collaborative filtering algorithms is how best to know about the preferences of a new user, who has rated none or few examples. Active learning provides effective strategies to select the most informative ratings though minimum interaction with new users. In this paper, we present a new method for actively acquiring ratings from new users. Using a co-clustering based collaborative filtering framework, we propose combining expected value of rating information with likelihood of getting ratings from the users to form the sample selection criterion. Empirical studies with two datasets of movie ratings show that the proposed method outperforms three popular active learning strategies for collaborative filtering.