Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Trust-based rating prediction for recommendation in web 2.0 collaborative learning social software
ITHET'10 Proceedings of the 9th international conference on Information technology based higher education and training
Improving PGP Web of Trust through the Expansion of Trusted Neighborhood
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Social contextual recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
Using structural information for distributed recommendation in a social network
Applied Intelligence
Materializing trust as an understandable digital concept
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Exploiting two-faceted web of trust for enhanced-quality recommendations
Expert Systems with Applications: An International Journal
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With the advent of online social networks, the trust-based approach to recommendation has emerged which exploits the trust network among users and makes recommendations based on the ratings of trusted users in the network. In this paper, we introduce a two dimensional trust model which dynamically gets updated based on users's feedbacks, in contrast to static trust values in current trust models. Explorability measures the extent to which a user can rely on recommendations returned by the social network of a trusted user. Dependability represents the extent to which a user's own ratings can be trusted by users trusting him directly and indirectly. We propose a method to learn the values of explorability and dependability from raw trust data and feedback expressed by users on the recommendations they receive. Positive feedback will increase the trust and negative feedback will decrease the trust among users. We performed an evaluation on the Epinions dataset, demonstrating that exploiting user feedback results in lower prediction error compared to existing trust-based and collaborative filtering approaches.