Abstract
AbstractOnline rating systems have been widely adopted by online trading communities to ban “bad” service providers and prompt them to provide “good” services. However, the performance of the online rating systems is easily compromised by various unfair ratings, e.g. balloting, badmouthing, and complementary unfair ratings. How to mitigate the influence of the unfair ratings remains an important issue in online rating systems. In this paper, we propose a novel entropy-based method to measure the rating quality as well as to screen the unfair ratings. Experimental results show that the proposed method is both effective and practical in alleviating the influence of different types of unfair ratings.KeywordsMean Square ErrorRating SystemMajority OpinionBeta DistributionLocal RatingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
