Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Communications of the ACM
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
SIAM Journal on Discrete Mathematics
Evaluation and Design of Online Cooperative Feedback Mechanisms for Reputation Management
IEEE Transactions on Knowledge and Data Engineering
A survey of trust and reputation systems for online service provision
Decision Support Systems
Fuzzy computational models for trust and reputation systems
Electronic Commerce Research and Applications
A probabilistic reputation model based on transaction ratings
Information Sciences: an International Journal
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With the growth of e-commerce, online reputation system has become the important character in many e-commerce sites. The trust between seller and customer established relying on the reputation system. This make the fairness and accuracy of reputation computing model are very important. Current, most reputation computing models cannot reach the objectives, because they didn't consider the number of objects' ratings. In this paper, we propose a novel computing model which use one-way random effects model. This model introduces the random effects, and considers the number of objects' ratings. the random effects is predicted by BLUP(Best linear unbiased Prediction).We have evaluated the difference of top k lists by this model from that by average model in real data sets, and proof the fairness and accuracy of this model using cases.