Strength of social influence in trust networks in product review sites
Some popular product review sites such as Epinions allow users to establish a trust network among themselves, indicating who they trust in providing product reviews and ratings. While trust relations have been found to be useful in generating personalised recommendations, the relations between trust and product ratings has so far been overlooked. In this paper, we examine large datasets collected from Epinions and Ciao, two popular product review sites. We discover that in general users who trust each other tend to have smaller differences in their ratings as time passes, giving support to the theories of homophily and social influence. However, we also discover that this does not hold true across all trusted users. A trust relation does not guarantee that two users have similar preferences, implying that personalised recommendations based on trust relations do not necessarily produce more accurate predictions. We propose a method to estimate the strengths of trust relations so as to estimate the true influence among the trusted users. Our method extends the popular matrix factorisation technique for collaborative filtering, which allow us to generate more accurate rating predictions at the same time. We also show that the estimated strengths of trust relations correlate with the similarity among the users. Our work contributes to the understanding of the interplay between trust relations and product ratings, and suggests that trust networks may serve as a more general socialising venue than only an indication of similarity in user preferences.