Learning adaptive trust strength with user roles of truster and trustee for trust-aware recommender systems

There are two key characteristics of users in trust relationships that have been well studied: (1) users trust their friends with different trust strengths and (2) users play multiple roles of trusters and trustees in trust relationships. However, few studies have considered both of these factors. Indeed, it is quite common for someone to respond to his/her friend that they trusted him/her, which indicates that there exist two kinds of information between each pair of users: the trust influence of trustee on truster and the feedback influence of truster on trustee. Considering this problem, we propose a novel adaptive method to learn the trust influence between users with multiple roles of truster and trustee for recommendation. First, we propose to introduce the concept of latent trust strength to learn adaptive role-based trust strength with limited values for each trust relationship between users. Second, because there is only one training example to learn each parameter of latent trust strength, we further propose two regularization methods by building relations between latent trust strength and user preferences to guide the training process of latent trust strength. After that, we develop a new recommendation method, RoleTS, by integrating the role-based trust strength into a previous recommendation model, TrustSVD, which considers both explicit and implicit information of trust and ratings. We also conduct a series of experiments to study the performance of the proposed method. Experimental results on two public real datasets demonstrate that the proposed method performs better than several state-of-the-art algorithms.