Collaborative filtering (CF) is one of the most well-known and commonly used technology for recommender systems. However, it suffers from inherent issues such as data sparsity. Many works have been done by used additional information such as user attributes, tags and social relationships to address these problems. We proposed an algorithm named OLrs (Opinion Leaders for Recommender System) base...

Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network ...

The information overflow of today’s information society can be overcome by the usage of recommender systems. Due to the fact that most recommender systems act as black boxes, trust in a system decrease, especially when a recommendation failed. Recommender systems usually don’t offer any insight into the systems logic and cannot be questioned as it is normal for a recommendation process between ...

Trust networks among users of a recommender system (RS) prove beneficial to the quality and amount of the recommendations. Since trust is often a gradual phenomenon, fuzzy relations are the pre-eminent tools for modeling such networks. However, as current trust-enhanced RSs do not work with the notion of distrust, they cannot differentiate unknown users from malicious users, nor represent incon...

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However,...

In this paper, we analyse the sustainability of social networks using STrust, our social trust model. The novelty of the model is that it introduces the concept of engagement trust and combines it with the popularity trust to derive the social trust of the community as well as of individual members in the community. This enables the recommender system to use these different types of trust to re...

In collaborative filtering recommender systems, users cannot get involved in the choice of their peer group. It leaves users defenseless against various spamming or “shilling” attacks. Other social Web-based systems, however, allow users to self-select trustworthy peers and build a network of trust. We argue that users self-defined networks of trust could be valuable to increase the quality of ...

The trust-aware recommender system (TARS) suggests the worthwhile information to the users on the basis of trust. Existing models of TARS use personalized rating prediction mechanisms, which can provide personalized services to each user, but they are computational very expensive. We therefore propose an efficient global rating prediction mechanism for TARS: we use the k-shell decomposition to ...