GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Improving collaborative filtering with trust-based metrics
Proceedings of the 2006 ACM symposium on Applied computing
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhanced Recommendations through Propagation of Trust and Distrust
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Key figure impact in trust-enhanced recommender systems
AI Communications - Recommender Systems
Building trust in electronic communities by mining web content
International Journal of Computational Science and Engineering
RCCtrust: A combined trust model for electronic community
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
Identifying influential reviewers for word-of-mouth marketing
Electronic Commerce Research and Applications
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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 connect to the network as soon as possible. But whom should new users connect to? Given the impact this choice has on the delivered recommendations, it is critical to guide newcomers through this early stage connection process. In this paper, we identify key figures in the trust network (in particular mavens, connectors and frequent raters) and investigate their influence on the coverage and accuracy of a collaborative filtering RS. Using a dataset from Epinions.com, we demonstrate that the generated recommendations for new user are more beneficial if they connect to an identified key figure compared to a random user.