Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration

摘要

Trust has been explored by many researchers in the past as a solution for assisting the process of recommendation production. In this work we are examining the feasibility of building networks of trusted users using the existing evidence that would be provided by a standard recommender system. As there is lack of models today that could help in finding the relationship between trust and similarity we build our own that uses a set of empirical equations to map similarity metrics into Subjective Logic trust. In this paper we perform evaluation of the proposed model as being a part of a complete recommender system. Finally, we present the interesting results from this evaluation that shows the performance and benefits of our trust modeling technique as well as its impact on the user community as it evolves over time.