디지털융복합연구 (Journal of Digital Convergence)

  • 제15권2호
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  • Pages.115-120
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  • 2017
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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협업필터링을 이용한 사회연결망서비스(SNS)용 온라인 평판시스템 신뢰도 향상에 관한 연구

Credibility Enhancement of Online Reputation Systems for SNS Using Collaborative Filtering Method

  • 조진형 (동양미래대학교 컴퓨터정보공학과) ;
  • 강환수 (동양미래대학교 컴퓨터정보공학과) ;
  • 김시우 (숭의여자대학교 가족복지과)
  • Cho, Jin-hyung (Dept. of Computer & Information Engineering, Dongyang Mirae University) ;
  • Kang, Hwan-Soo (Dept. of Computer & Information Engineering, Dongyang Mirae University) ;
  • Kim, Sea-Woo (Dept. of Family Welfare, Soongeui Women's College)
  • 투고 : 2016.11.28
  • 심사 : 2017.02.20
  • 발행 : 2017.02.28
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초록

본 연구는 온라인 사회연결망서비스(SNS)를 기반으로 하는 전자상거래, 즉 소셜 커머스 상의 콘텐츠 또는 상품광고에 대하여 형성되는 사용자 평판 형성의 신뢰도를 강화시키는 기법을 도출하고자 하는 데에 목적을 두고 있다. 온라인 평판정보는 소비자들의 의사결정에 중요한 요인으로 작용하고 있음에도 불구하고 평가자의 주관적 성향에 의존적이고 또한 이러한 평가를 자신 또는 판매자의 이익을 위해 악용하는 경우가 있기 때문에 온라인 여론 형성의 신뢰도에 문제가 있을 수 있다. 따라서, 본 연구에서는 협업필터링 기법을 기반으로 각 사용자 평판에 차별적인 가중치를 부여하는 방식을 적용해 SNS용 온라인 평판시스템 신뢰도를 향상시키고자 하였다. 본 연구의 결과는 사용자 평가 값에 각 개인의 신뢰도 가중치를 반영함으로써 좀 더 신뢰할 수 있는 평판결과를 제시할 수 있고, 아울러 특정집단의 이익을 위해 사용자 평판시스템을 악용하는 것을 막는 데 기여할 수 있을 것으로 기대된다.

Online reputation systems for social network services(SNS) aggregate users' feedback and estimate the reputation of contents or providers. The aim of this research is to enhance credibility of the online reputation system on the SNS based e-Commerce(we called it as social commerce). SNS users usually refer to evaluations from other users who bought the products before. Most social commerce sites provide reputation system to help their customer make a decision, but sometimes we can't believe the reputation because the reputation is too subjective and the seller can deceive the customer for sales promotion. Threrefore, we usually use just the average value to show the general customer's evaluation result. We applied collaborative filtering method to give more weighting to the users who have evaluated correctly in the past. As a result, we could get more accurate evaluation results by considering each customers' credibility value that was computed by collaborative filtering.

키워드

참고문헌

  1. Josang, A., Ismail, R., Boyd, C., "A survey of trust and reputation systems for online service provision." Decision Support Systems, Vol. 43, No. 2, pp.618-644, 2007. https://doi.org/10.1016/j.dss.2005.05.019
  2. Song E., "A Study on the Case Analysis of Customer Reputation based on Big Data." Journal of Korea Information and Communication Engineering Society, Vol. 17, No. 10, pp. 2439-2446, 2013. https://doi.org/10.6109/jkiice.2013.17.10.2439
  3. H. Kong, Song E., "A Study on Hotel Customer Reputation Analysis based on Big Data." Journal of Digital Contents Society, Vol. 15, No. 2, pp. 219-225, 2014. https://doi.org/10.9728/dcs.2014.15.2.219
  4. Gayatri Swamynathan. et al., "The design of a reliable reputation system." Electronic Commerce Research, Vol. 10, No. 3-4, pp. 239-270, 2010. https://doi.org/10.1007/s10660-010-9064-y
  5. Liu Ling, Munro Malcolm, "Systematic analysis of centralized online reputation systems." Decision Support Systems, Vol. 52, No. 2, pp. 438- 449, 2011. https://doi.org/10.1016/j.dss.2011.10.003
  6. Stephen C. Hayne, Haonan Wang, Lu Wang, "Modeling Reputation as a Time‐Series: Evaluating the Risk of Purchase Decisions on e-Bay." Decision Sciences, Vol. 46, No. 6, pp. 1077-1107p, 2015. https://doi.org/10.1111/deci.12156
  7. Kevin Hoffman. et al., "A survey of attack and defense techniques for reputation systems." ACM Computing Surveys, Vol. 42, No. 1, pp.1-31, 2009.
  8. Shangguang Wang. et al., "Enhanced User Context- Aware Reputation Measurement of Multimedia Service." ACM Transactions on Multimedia Computing, Communications, and Applications(TOMM), Vol. 12, No. 4, pp.1-18, 2016.
  9. Dellarocas, C., "The digitization of word-of-mouth: promise and challenges of online feedback mechanisms." Management Science, Vol. 49, No.10, pp.1407-1424, 2003. https://doi.org/10.1287/mnsc.49.10.1407.17308
  10. Ekstrom, M., Bjornsson, H., Nass, C., "A reputation mechanism for business-to-business electronic commerce that accounts for rater credibility." Journal of Organizational Computing and Electronic Commerce, Vol.15, No.1, pp. 1-18, 2005. https://doi.org/10.1207/s15327744joce1501_1
  11. Chen, M., Singh, J., "Computing and using reputations for internet ratings." Proceedings of the 3rd ACM Conference on Electronic Commerce (EC 01), pp. 246-247, 2001.
  12. Herlocker J. L. et al., "Evaluating Collaborative Filtering Recommender Systems." ACM Transactions on Information Systems, Vol.22, No.1, pp. 5-53, 2004. https://doi.org/10.1145/963770.963772
  13. Adomavicius G., Tuzhilin A., "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions." IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, pp. 734-749, 2005. https://doi.org/10.1109/TKDE.2005.99
  14. Massa, P., Avesani, P., "Trust-aware collaborative filtering for recommender systems." Proceedings of CoopIS 04, pp. 492-508, 2004.
  15. Cho J., Kwon K., Park Y., "Collaborative Filtering Using Dual Information Sources." IEEE Intelligent Systems, Vol.22, No. 3, pp. 30-38, 2007. https://doi.org/10.1109/MIS.2007.48