On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets

Abstract

One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy.