An Improved Recommender System based on Multi-criteria Clustering Approach

Abstract Traditional collaborative filtering based recommender systems deal with the two-dimensional user-item rating matrix where users have rated a set of items into the system. Although traditional recommender systems are widely adopted but they are unable to generate effective recommendations in case of multi-dimensionality i.e. multi-criteria ratings, contextual information, side information etc. The curse of dimensionality is the major issue in the recommendation systems. Therefore, in this paper, we proposed a clustering approach to incorporate multi-criteria ratings into traditional recommender systems effectively. Furthermore, we compute the intra-cluster user similarities using a Mahalanobis distance method in order to make more accurate recommendations and compared the proposed approach with the traditional collaborative filtering based method using Yahoo! Movies dataset.