Surprising Discoveries by Recommender Systems

Recommender systems apply data mining techniques to predict users’ interest on information, products and services among a tremendous amount of available items. Key to the success of recommender systems is user satisfaction. That cannot be achieved only by accurate recommendations. Recent work has focused on new measures that are beyond the accuracy of recommender systems. Serendipity is one of these measures. Serendipity occurs when one finds an interesting discovery while searching for something else. This paper is an attempt to provide an overview of the state of the art in new dimension of recommender system research. This paper will present an outlook on the existing research carried out in this area. In the end, the paper will be concluded by listing future trends in this research area.