Key Figure Impact in Trust-Enhanced Recommender Systems

From TrustLet, a free, collaborative project for collecting and analyzing information about trust metrics.

Key Figure Impact in Trust-Enhanced Recommender Systems [1] is a paper that shows how the user cold start problem in a trust network of a recommender system can be alleviated by connecting to identified key figures instead of random users.

This paper is an extended version of the SAC08 paper Whom Should I Trust? The Impact of Key Figures on Cold Start Recommendations, containing new evaluation measures (based on social network analysis measures) and new results.

[edit] Abstract

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 network, but choosing whom to connect to is often a difficult task. 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 several classes of key figures in the trust network, namely mavens, frequent raters and connectors. Furthermore, we introduce measures to assess the influence of these users on the amount and the quality of the recommendations delivered by a trust-enhanced collaborative filtering recommender system. Experiments on a dataset from Epinions.com support the claim that generated recommendations for new users are more beneficial if they connect to an identified key figure compared to a random user.