Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

A Unified Framework for Reputation Estimation in Online Rating Systems / 2670
Guang Ling, Irwin King, Michael R. Lyu

Online rating systems are now ubiquitous due to the success of recommender systems. In such systems, users are allowed to rate the items (movies, songs, commodities) in a predefined range of values. The ratings collected can be used to infer users' preferences as well as items' intrinsic features, which are then matched to perform personalized recommendation. Most previous work focuses on improving the prediction accuracy or ranking capability. Little attention has been paid to the problem of spammers or low-reputed users in such systems. Spammers contaminate the rating system by assigning unreasonable scores to items, which may affect the accuracy of a recommender system. There are evidences supporting the existence of spammers in online rating systems. Reputation estimation methods can be employed to keep track of users' reputation and detect spammers in such systems. In this paper, we propose a unified framework for computing the reputation score of a user, given only users' ratings on items. We show that previously proposed reputation estimation methods can be captured as special cases of our framework. We propose a new low-rank matrix factorization based reputation estimation method and demonstrate its superior discrimination ability.