Abstract：Transaction and rating data can be used to evaluate credit as a key underlying service provided by online transaction platforms. However, credit evaluation methods pay little attention to user rating preferences, so rating manipulations can expand arbitrarily. A method is given here to determine transaction rating credibility based on group preferences. This method uses the K-means clustering algorithm to divide all the users into three groups and analyzes and validates each group's rating preferences. Then, the algorithm provides three steps to determine the credibility of ratings on different levels for different user groups based on these preferences. This paper also provides a strategy to dynamically update the group division and credibility to effectively restrict credit manipulation.
陈元琳, 柴跃廷, 刘义, 徐扬. 基于群体偏好的交易评价可信度[J]. 清华大学学报（自然科学版）, 2015, 55(5): 558-564,571.
CHEN Yuanlin, CHAI Yueting, LIU Yi, XU Yang. Transaction rating credibility based on user group preference. Journal of Tsinghua University(Science and Technology), 2015, 55(5): 558-564,571.
Liu Y. Word of mouth for movies: Its dynamics and impact on box office revenue [J]. Journal of Marketing, 2006, 70(3): 74-89.
[2]
Hennig-Thurau T, Gwinner K P, Walsh G, et al. Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet [J]. Journal of Interactive Marketing, 2004, 18(1): 38-52.
[3]
Bansal H S, Voyer P A. Word-of-mouth processes within a services purchase decision context [J]. Journal of Service Research, 2000, 3(2): 166-177.
[4]
Gupta P, Harris J. How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective [J]. Journal of Business Research, 2010, 63(9-10): 1041-1049.
[5]
Zhang Y, Bian J, Zhu W. Trust fraud: A crucial challenge for China's e-commerce market [J]. Electronic Commerce Research and Applications, 2013, 12(5): 299-308.
[6]
Resnick P, Kuwabara K, Zeckhauser R, et al. Reputation systems [J]. Communications of the ACM, 2000, 43(12): 45-48.
[7]
Mcknight H, Kacmar C. Factors of information credibility for an Internet advice site [C]//Proceedings of the 39th Annual Hawaii International Conference on System Sciences. Piscataway, NJ, USA: IEEE, 2006, 6: 113b-113b.
[8]
Kusumasondjaja S, Shanka T, Marchegiani C. Credibility of online reviews and initial trust: The roles of reviewer's identity and review valence [J]. Journal of Vacation Marketing, 2012, 18(3): 185-195.
[9]
Xu Q. Should I trust him? The effects of reviewer profile characteristics on eWOM credibility [J]. Computers in Human Behavior, 2014, 33: 136-144.
[10]
Patton M, J Euclid FrakturxCp sang A. Technologies for trust in electronic commerce [J]. Electronic Commerce Research, 2004, 4(1-2): 9-21.
[11]
Pan Y, Zhang J Q. Born unequal: A study of the helpfulness of user-generated product reviews [J]. Journal of Retailing, 2011, 87(4): 598-612.
[12]
Dellarocas C. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior [C]//Proceedings of the 2nd ACM Conference on Electronic Commerce. New York, NY: ACM, 2000: 150-157.
[13]
Ghose A, Ipeirotis P G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics [J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(10): 1498-1512.
[14]
Picazo-Vela S, Chou S Y, Melcher A J, et al. Why provide an online review? An extended theory of planned behavior and the role of big-five personality traits [J]. Computers in Human Behavior, 2010, 26(4): 685-696.
[15]
Li H, Liu Y, Suomi R. Exploring the factors motivating e-service users' WOM behaviour [J]. International Journal of Services Technology and Management, 2013, 19(4): 187-200.
[16]
Epstein S. The stability of behavior: I. On predicting most of the people much of the time [J]. Journal of Personality and Social Psychology, 1979, 37(7): 1097-1126.
[17]
Ajzen I. The theory of planned behavior [J]. Organizational Behavior and Human Decision Processes, 1991, 50(2): 179-211.
[18]
Mcauley J, Leskovec J. Hidden factors and hidden topics: Understanding rating dimensions with review text [C]//Proceedings of the 7th ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2013: 165-172.
2015, Vol. 55 
