Personality-based active learning for collaborative filtering recommender systems

Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user’s personality - using the Five Factor Model (FFM) - in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, contextaware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.