Influence-based collaborative active learning

Neil Rubens, Masashi Sugiyama

Abstract: In order to learn a user's preferences in collaborative recommender systems it is crucial to select the most informative items for a user to rate. For example, rating a popular item will provide little discriminative information about user's preferences since most users like popular items. Existing approaches select the most informative items based primarily on items' uncertainty, but tend to ignore an important metric of coverage - the number of items for which we are able to accurately estimate preferences. Selecting an item based only on uncertainty will reduce the uncertainty of the selected item, but will not necessarily reduce the uncertainty of other items - which is the ultimate goal. Therefore, in order to reduce the uncertainty over all items, we propose to select items that are not only uncertain but are also influential. Experimental results demonstrate the advantages of the proposed approach.