Patricia Victor

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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)
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)
Collaboration, interaction and information sharing are the main driving forces of the current generation of web applications referred to as ‘Web 2.0’ [47]. Well-known examples of this emerging trend include weblogs (online diaries or journals for sharing ideas instantly), Friend-Of-A-Friend1 (FOAF) files (machine-readable documents describing basic(More)
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)
Trust networks are social networks in which users can assign trust scores to each other. In order to estimate these scores for agents that are indirectly connected through the network, a range of trust score aggregators has been proposed. Currently, none of them takes into account the length of the paths that connect users; however, this appears to be a(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)
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)
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)