Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Method of Seller Reputation Computation Based on Rating Separation in e-Marketplace

평점 분리 기법을 이용한 e마켓플레이스의 판매자 평판 계산 방안

  • 오현교 (한양대학교 컴퓨터소프트웨어) ;
  • 노유한 (한양대학교 컴퓨터소프트웨어) ;
  • 김상욱 (한양대학교 컴퓨터소프트웨어) ;
  • 박선주 (연세대학교 경영)
  • Received : 2015.05.28
  • Accepted : 2015.07.16
  • Published : 2015.10.15
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Abstract

Most e-marketplaces build a reputation system that provides potential buyers with reputation scores of sellers in order for buyers to identify the sellers that are more reliable and trustworthy. The reputation scores are computed based on the aggregation of buyers' ratings. However, when these ratings are used to compute the reputation scores, the existing reputation systems do not make a distinction according to the following two criteria: the capability of the seller and the quality of an item. We claim that a reputation system needs to separate the two criteria in order to provide more precise information about the seller. In this paper, we propose a method to compute seller's reputation by separating the rating into the seller's score and the item's score. The proposed method computes the reputation of the seller's capability by using only the 'seller's score' and helps potential buyers to find reliable sellers who provide fast delivery and better service. In experiments, we propose a simulation strategy that reflects the real life of an E-marketplace and verify the effectiveness of our method by using the generated simulation data.

e-마켓플레이스는 구매자들이 보다 신뢰할 수 있는 판매자와 거래할 수 있도록 평판 시스템(Reputation system)을 구축하여 예비 소비자들에게 판매자의 평판을 제공한다. 판매자의 평판은 소비자의 평점을 기반으로 산출되는데 이 때 소비자의 평가 요소로는 판매자의 행동에 대한 평가와 상품에 대한 평가가 있다. 기존의 평판 계산 방안들은 구매자의 평점이 두 가지의 평가가 혼합된 점수라는 것을 인지하지 못한 채로 판매자의 평판을 산출한다. 본 논문에서는 소비자 평점을 판매자 점수와 상품 점수로 분리한 후 오직 '판매자의 점수'만을 이용하는 평판 계산 방안을 제안한다. 제안하는 방안은 판매자의 점수만을 이용하여 판매자의 능력에 대한 평판만을 제공하는 방안으로 예비 소비자들이 빠른 배송과 친절한 서비스를 제공하는 판매자를 선택할 수 있도록 돕는다. 실험에서는 실제 e-마켓 플레이스의 현실성을 반영한 시뮬레이션 방안을 제안한다. 생성된 시뮬레이션 데이터를 기반으로 진행하는 실험을 통해 제안하는 방법의 우수성을 입증한다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. [Online]. Available: http://en.wikipedia.org/wiki/EMarketPlace
  2. A. Josang, R. Ismail, and C. Boyd, "A survey of trust and reputation systems for online service provision," Decision Support Systems, Vol. 43, Issue 2, pp. 618-644, 2007. https://doi.org/10.1016/j.dss.2005.05.019
  3. A. M. Kramer, "Self-selection Bias in Reputation Systems," Trust Management, Vol. 238, pp. 255-268, 2007. https://doi.org/10.1007/978-0-387-73655-6_17
  4. P. Resnick, R. Zeckhauser, J. Swanson, and K. Lockwood, "The Value of Reputation on eBay: A Controlled Experiment," Experimental Economics, Vol. 9, Issue 2, pp. 79-101, 2006. https://doi.org/10.1007/s10683-006-4309-2
  5. L. Cabral and A. Hortacsu, "The Dynamics of Seller Reputation: Theory and Evidence from eBay," The Journal of Industrial Economics, Vol. 58, Issue 1, pp. 54-78, 2010. https://doi.org/10.1111/j.1467-6451.2010.00405.x
  6. D. Houser and J. Wooders, "Reputation in Auctions: Theory, and Evidence from eBay," Journal of Economics & Management Strategy, Vol. 15, Issue 2, pp. 353-369, 2006. https://doi.org/10.1111/j.1530-9134.2006.00103.x
  7. A. Whitby, A. Josang and J. Indulska, "Filtering Out Unfair Ratings in Bayesian Reputation Systems," The Icfain Journal of Management Research, Vol. 4, No. 2, pp. 48-64, 2005.
  8. W. T. L Teacy, J. Patel, N. R. Jennings, and M. Luck, "Travos: Trust and Reputation in the Context of Inaccurate Information Sources," Autonomous Agents and Multi-Agent Systems, Vol. 12, No. 2, pp. 183-198, 2006. https://doi.org/10.1007/s10458-006-5952-x
  9. J. Zhang and R. Cohen, "Evaluating the Trustworthiness of Advice about Seller Agents in Emarketplaces: A Personalized Approach," Electronic Commerce Research and Applications, Vol. 7, No. 3, pp. 330-340, 2008. https://doi.org/10.1016/j.elerap.2008.03.001
  10. S. Liu et al., "iCLUB: an Integrated Clustering-based Approach to Improve the Robustness of Reputation Systems," Proc. of the 10th Int'l Joint Conf. on Autonomous Agents and Multiagent Systems, pp. 1151-1152, 2011.
  11. C. Dellarocas, "Immunizing Online Reputation Reporting Systems against Unfair Ratings and Discriminatory Behavior," Proc. of the 2nd ACM conf. on Electronic commerce, pp. 150-157, 2000.
  12. A. Jsang and R. Ismail, "The Beta Reputation System," Proc. of the 15th bled electronic commerce conf., pp. 41-55, 2002.
  13. L. Zhang, s. Jiang, J. Zhang, and W. K. Ng, "Robustness of Trust Models and Combinations for Handling Unfair Ratings," Trust Management VI, Vol. 374, pp. 36-51, 2012.
  14. H. Fang, J. Zhang, M. Sensoy, N. M. Thalmann, "SARC: Subjectivity Alignment for Reputation Computation," Proc. of the 11th Int'l Conf. on Autonomous Agents and Multiagent Systems, pp. 1365-1366, 2012.
  15. K. Regan, P. Poupart, and R. Cohen, "Bayesian Reputation Modeling in E-marketplaces Sensitive to Subjectivity, Deception and Change," Proc. of the National Conf. on Artificial Intelligence, 2006.
  16. A. Irissappane, S. Jiang, and J. Zhang, "Towards a Comprehensive Testbed to Evaluate the Robustness of Reputation Systems Against Unfair Rating Attack," User Modeling Adaptation and Personalization Workshops, 2012.
  17. P. Chandrasekaran and B. Esfandiari, "A Model for a Testbed for evaluation reputation system," Proc. of Int'l Conf. on Trust, Security and Privacy in Computing and Communications, pp. 296-303, 2011.
  18. R. Kerr and R. Cohen, "TREET: The Trust and Reputation Experimentation and Evaluation Testbed," Electronic Commerce Research, Vol. 10, Issue 3, pp. 271-290, 2010. https://doi.org/10.1007/s10660-010-9056-y