Personalized active learning for collaborative filtering
Collaborative Filtering (CF) requires user rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most informative set of training examples through minimum interactions with the users. Current active learning approaches in CF make an implicit and unrealistic assumption that a user can provide rating for any queried item. This paper introduces a new approach to the problem which does not make such an assumption. We personalize active learning for the user, and query for only those items which the user can provide rating for. We propose an extended form of Bayesian active learning and use the Aspect Model for CF to illustrate and examine the idea. A comparative evaluation of the new method and a well established baseline method on benchmark datasets shows statistically significant improvements with our method over the performance of the baseline method that is representative for existing approaches which do not take personalization into account.
SIGIR '08