Generalized Preference Learning for Trust Network Inference

Trust inference is essential in a plethora of data mining and machine learning applications. Unfortunately, conventional approaches to trust inference assume trust networks are available, while in practice they must be derived from social network features. This is however a difficult task which has to cope with challenges relating to scarcity, redundancy and noise in the available user interactions and other social network features. In this work, we introduce the new problem of Trust Network Inference (TNI), that is, inferring a trust network from a sequence of timestamped interaction networks. To solve the TNI problem, we propose a principled approach based on a preference learning paradigm, under a preference-based racing formulation. The proposed approach is suitable for addressing the above challenges, moreover it is versatile (i.e., independent from the social network platform) and flexible w.r.t. the use of topological and content-based information. Extensive experimental evaluation focusing on two distinct ground-truth scenarios, has provided evidence of the meaningfulness and uniqueness of our TNI approach, which can be regarded as key-enabling for any application that requires to handle a trust network associated with a social environment.