Improving top-n recommendation techniques using rating variance

One of the goals in recommender systems is to recommend those items to each user that maximize the user's utility. In this study, we propose new approaches which, in conjunction with any existing recommendation technique, can improve the top-N item selection by taking into account rating variance. We empirically demonstrate how these approaches work with several recommendation techniques, increasing the accuracy of recommendations. We also show how these approaches can generate more personalized recommendations, as measured by the diversity metric. As a result, users can be given a better control to choose whether to receive recommendations with higher accuracy or higher diversity.