Enabling topic-level trust for collaborative information sharing

Abstract

As a consequence of the exponential growth of Internet and its services, including social applications fostering collaboration on the Web, information sharing had become pervasive. This caused a crescent need of more powerful tools to help users with the task of selectinginteresting resources. Recommender systems have emerged as a solution to evaluate the quality of massively usergenerated contents in open environments and provide recommendations based not only on the user interests but also on the opinions of people with similar tastes. In addition to interest similarity, however, trustworthiness is a factor thatrecommenders have to consider in the selection of reliable peers for collaboration. Most approaches in this regard estimates trust base on global user profile similarity orhistory of exchanged opinions. In this paper, we propose a novel approach for agent-based recommendation in which trust is independently learned and evolved for each pair ofinterest topics two users have in common. Experimental results show that agents learning who to trust about certain topics reach better levels of precision than considering interest similarity exclusively.