Impact of Trust on the Performance of a Recommendation System in a Social Network

Social agents naturally use their social and professional networks to filter information by trustworthiness. In this paper, we present a model of an automated distributed recommendation system on a social network and we investigate how the dynamics of trust among agents affect the performance of the system. Agents search their social network for recommendations on items to be consumed and the propagation of the query through agents at several degrees of separation enhances the efficiency of their search. Moreover, agents have heterogeneous preferences so that trust between neighbours can be used to filter information coming from remote agents. We identify the range of the density of the network and the degree of heterogeneity of agent preferences in which trust improves the performance of the recommendation system.