An empirical analysis of implicit trust metrics in recommender systems

Abstract

Recommender system is an intelligent solution to information overload problem. Classical collaborative filtering based recommender system suffers from cold start and data sparsity problems. Incorporation of trust in classical recommender systems has potential to improve the overall performance of recommender system. Trust has been enormously researched and its influence is manifested in recommender systems. Because of unavailability of explicit trust information, various implicit trust metrics are developed to deduce trust from user's online behavior. In this paper, we have conducted an empirical study of six implicit trust metrics on two different real world datasets. A comparative analysis of these metrics with classical user based collaborative filtering is performed.