The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
UBICOMM '07 Proceedings of the International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mechanizing social trust-aware recommenders with T-index augmented trustworthiness
TrustBus'10 Proceedings of the 7th international conference on Trust, privacy and security in digital business
Achieving optimal privacy in trust-aware social recommender systems
SocInfo'10 Proceedings of the Second international conference on Social informatics
Building trust communities using social trust
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Mining Divergent Opinion Trust Networks through Latent Dirichlet Allocation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
A survey of trust in social networks
ACM Computing Surveys (CSUR)
Exploiting two-faceted web of trust for enhanced-quality recommendations
Expert Systems with Applications: An International Journal
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Collaborative Filtering based on similarity suffers from a variety of problems such as sparsity and scalability. In this paper, we propose an ontological model of trust between users on a social network to address the limitations of similarity measure in Collaborative Filtering algorithms. For enhancing the constructed network of users based on trust, we introduce an estimate of a user’s trustworthiness called T-index to identify and select neighbors in an effective manner. We employ T-index to store raters of an item in a so-called TopTrustee list which provides information about users who might not be accessible within a predefined maximum path length. An empirical evaluation shows that our solution improves both prediction accuracy and coverage of recommendations collected along few edges that connect users on a social network by exploiting T-index. We also analyze effect of T-index on structure of trust network to justify the results.