A Unified Framework for Reputation Estimation in Online Rating Systems
Refereed conference paper presented and published in conference proceedings

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AbstractOnline 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.
All Author(s) ListLING Guang, KING Kuo Chin Irwin, LYU Rung Tsong Michael
Name of ConferenceThe 23rd International Joint Conference on Articial Intelligence
Start Date of Conference03/08/2013
End Date of Conference09/08/2013
Place of ConferenceBeijing
Country/Region of ConferenceChina
Proceedings TitleProceedings of the Twenty-Third International Joint Conference on Artificial Intelligence
Pages2670 - 2676
LanguagesEnglish-United Kingdom

Last updated on 2020-02-09 at 00:53