Recommendations Biases and Beyond-Accuracy Objectives in Collaborative Filtering

Recommender systems research, traditionally focused on accuracy, is currently paying more and more attention to additional factors for evaluating the perceived quality and usefulness of recommendation lists. In this paper, we present a survey of the most important dimensions, other than accuracy, usually taken into account in the collaborative filtering literature. We survey beyond-accuracy objectives, i.e. novelty, diversity and serendipity, and the main techniques for increasing them. Moreover, we discuss possible undesired biases occurring in collaborative filtering algorithms, and how to effectively deal with them.