Effect of distrust propagation to enhance the performance of trust based recommender systems

Abstract

Trust aware recommender systems (TARS) are a branch of the most popular technique of recommender systems that is Collaborative Filtering. Lots of studies used trust as an enhancement factor for improve the accuracy of TARS, but the main problem of using trust is sparsity and also scalability problem for updating explicit data for new users, trust propagation used as a solution for solving the problem, but recent studies shows that using distrust values can also be useful, but same as trust and even worse, sparsity is a critical problem of dataset for using distrust. In this paper we used friend of friend and enemy of enemy concept as a way for propagating distrust and exploit new implicit relations for solving the sparsity problem and also improving the accuracy of recommendations. The results shows that propagating of distrust values is beneficial in enhancement of trust ware recommender systems.