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Dependable filtering: Philosophy and realizations

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., “I trust those who agree with me”), or social ties (i.e., “I trust my friends, and the people that my friends trust”). The former criterion aims at identifying concordant users, but is subject to abuse by malicious behaviors. The latter aims at detecting well-intentioned users, but fails to capture the natural subjectivity of tastes. In this article, we propose a new definition of trusted recommenders, addressing those users that are both well-intentioned and concordant. Based on this characterisation, we propose a novel approach to information filtering that we call dependable filtering. We describe alternative algorithms realizing this approach, and demonstrate, by means of extensive performance evaluation on a variety of real large-scale datasets, the high degree of both accuracy and robustness they entail.