Trust and nuanced profile similarity in online social networks

Abstract

Online social networks, where users maintain lists of friends and express their preferences for items like movies, music, or books, are very popular. The Web-based nature of this information makes it ideal for use in a variety of intelligent systems that can take advantage of the users' social and personal data. For those systems to be effective, however, it is important to understand the relationship between social and personal preferences. In this work we investigate features of profile similarity and how those relate to the way users determine trust. Through a controlled study, we isolate several profile features beyond overall similarity that affect how much subjects trust hypothetical users. We then use data from FilmTrust, a real social network where users rate movies, and show that the profile features discovered in the experiment allow us to more accurately predict trust than when using only overall similarity. In this article, we present these experimental results and discuss the potential implications for using trust in user interfaces.