Fuzzy Computational Models of Trust and Distrust for Enhanced Recommendations

Trust models have received considerable attention in the recent past and have been employed in many of today's most successful recommender systems (RSs) for alleviating sparsity by enhancing their interuser connectivity obtained from historical preference data and to address the cold start problem. However, the incorporation of distrust in addition to trust for improved recommendations has not been analyzed fully because of the absence of publically available data sets containing gradual information about trust and distrust concepts. Our work in this paper is an attempt toward introducing recommendation strategies exploiting both the trust and the distrust in the RSs to further enhance their quality of recommendations through fuzzy computational models for trust and distrust. Since trust and distrust concepts are gradual phenomenon, therefore these can be represented more naturally by fuzzy logic using linguistic expressions. The contributions of this paper are three fold: First, we propose fuzzy computational models for both the trust and distrust concepts through similarity as well as knowledge factors based on linguistic expressions. Second, we suggest appropriate propagation and aggregation operators to deal with the data sparsity. Finally, we present a comparative analysis of proposed recommendation strategies utilizing both the trust and distrust concepts.