SOFIA: Social filtering for robust recommendations

Digital content production and distribution has radically changed our business models. An unprecedented volume of supply is now on offer, whetted by the demand of millions of users from all over the world. Since users cannot be expected to browse through millions of different items to find what they might like, filtering has become a popular technique to connect supply and demand: trusted users are first identified, and their opinions are then used to create recommendations. In this domain, users' trustworthiness has been measured according to one of the following two criteria: taste similarity (i.e., "trust those who agree with me"), or social ties (i.e., "trust my friends, and the people that my friends trust"). The former criterion aims at identifying competent users, but is subject to abuse by malicious behaviours. The latter aims at detecting well-intentioned users, but fails to capture the natural subjectivity of tastes. We argue that, in order to be trusted, users must be both well-intentioned and competent. Based on this observation, we propose a novel approach that we call social filtering. We describe SOFIA, an algorithm realising this approach, and validate its performance, in terms of accuracy and robustness, on two real large-scale datasets. © 2008 International Federation for Information Processing.