Beyond rating prediction accuracy: on new perspectives in recommender systems

This paper proposes a number of studies in order to move recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored paradigms and also propose new approaches aiming at more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. In particular, we move our focus from even more accurate rating predictions and aim at offering a holistic experience to the users by avoiding the over-specialization of generated recommendations and providing the users with sets of non-obvious but high quality recommendations that fairly match their interests and they will remarkably like.