Exploring the use of explicit trust links for filtering recommenders: A study on epinions.com

Abstract

The majority of recommender systems predict user preferences by relating users with similar attributes or taste. Prior research has shown that trust networks improve the accuracy of recommender systems, predominantly using algorithms devised by individual researchers. In this work, omitting any specific trust inference algorithm, we investigate how useful it might be if explicit trust relationships are used to select the best neighbors or predictors, to generate accurate recommendations. We conducted a series of evaluations using data from Epinions.com, a popular collaborative reviewing system. We find that, for highly active users, using trusted sources as predictors does not give more accurate recommendations compared to the classic similarity-based collaborative filtering scheme, except in improving the precision to recommend items that are of users’ liking. This cautions against the intuition that inputs from trusted sources would always be more accurate or helpful. The use of explicit trust links, however, provides a slight gain in prediction accuracy when it comes to the less active users. These findings highlight the need and potential to adapt the use of trust information for different groups of users, besides to better understand trust when employing it in the recommender systems. Parallel to the trust criterion, we also investigated the effects of requiring the candidate predictors to have an equal or higher experience level.