Modeling trust for recommender systems using similarity metrics

Abstract

In this paper we present novel techniques for modeling trust relationships that can be used in recommender systems. Such environments exist with the voluntary collaboration of the community members who have as a common purpose the provision of accurate recommendations to each other. The performance of such systems can be enhanced if the potential trust between the members is properly exploited. This requires that trust relationships are appropriately established between them. Our model provides a link between the existing knowledge, expressed in similarity metrics, and beliefs which are required for establishing a trust community. Although we explore this challenge using an empirical approach, we attempt a comparison between the alternative candidate formulas with the aim of finding the optimal one. A statistical analysis of the evaluation results shows which one is the best. We also compare our new model with existing techniques that can be used for the same purpose. © 2008 International Federation for Information Processing.