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Trust and distrust are two increasingly important metrics in social networks, reflecting users' attitudes and relationships towards each other. In this paper , we study the indirect derivation of these metrics' values for users that do not know each other, but are connected through the network. In particular, we study bilattice-based aggregation approaches(More)
Generating adequate recommendations for newcomers is a hard problem for a recommender system (RS) due to lack of detailed user profiles and social preference data. Empirical evidence suggests that the incorporation of a trust network among the users of the RS can leverage such 'cold start' (CS) recommendations. Hence, new users should be encouraged to(More)
When a Web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining user ratings with information from the trust network. These are the so-called trust-enhanced recommendation systems. While research on the incorporation of(More)
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(More)
Recommender systems that incorporate a social trust network among their users have the potential to make more per-sonalized recommendations compared to traditional collab-orative filtering systems, provided they succeed in utilizing the additional trust and distrust information to their advantage. We compare the performance of several well-known(More)
Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recom-mender techniques that are discussed in the(More)
Social networks in which users or agents are connected to other agents and sources by trust relations are an important part of many web applications where information may come from multiple sources. Trust recommendations derived from these social networks are supposed to help agents develop their own opinions about how much they may trust other agents and(More)
Collaborative filtering recommender systems are typically unable to generate adequate recommendations for newcomers. Empirical evidence suggests that the incorporation of a trust network among the users of a recommender system can significantly help to alleviate this problem. Hence, users are highly encouraged to connect to other users to expand the trust(More)
The ability for a user to accurately estimate the amount of trust to be placed in a peer user is gaining more and more attention in social network applications. Trust aggregation provides this ability by identifying paths that connect users in the network, and by merging trust opinions expressed by users along these paths. However, as individual trust(More)