Trust-aware Bootstrapping of Recommender Systems

Recommender Systems (RS) suggest to users items they might like such as movies or songs. However they are not able to generate recommendations for users who just registered, in fact bootstrapping Recommender Systems for new users is still an open challenge. While traditional RSs exploit only ratings provided by users about items, Trust-aware Recommender Systems let the user express also trust statements, i.e. their subjective opinions about the usefulness of other users. In this paper we analyze the relative benefits of asking new users either few ratings about items or few trust statements about other users for the purpose of bootstrapping a RS ability to generate recommendations. We run experiments on a large real world dataset derived from the online Web community of Epinions.com. The results clearly indicate that while traditional RS algorithms exploiting ratings on items fail for new users, asking few trust statements to a new user is instead a very effective strategy able to quickly let the RS generate many accurate items recommendations.