Online communities, 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 a 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 paper, we present these experimental results and
discuss the potential implications for social networking and intelligent systems.
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