User and Item Pattern Matching in Multi-criteria Recommender Systems

Information on the ratings of several features of items can be deployed to improve the quality of recommendations in recommender systems by incorporating them into similarity calculation between any two users or two items. However, the incremental information of these features has important impacts on recommender systems. For example, the complexity of similarity calculation is increased and more resources are consumed during the process for generating recommendations. In this paper, we propose several techniques by using this information to provide relevant recommendations and to reduce the complexity in similarity computation by directly matching between preferences of user and the strength of item features.