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Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
It is a challenge to design a well balanced reputation system for an environment with millions of users. A reputation system must also represent user reputation as a value which is simple and easy to compare and will give users straightforward suggestions who to trust. Since reputation systems rely on feedbacks given by users, it is necessary to collect unbiased feedbacks. In this paper we present a controversial, yet innovative reputation system. Hidden and Indirect (Probabilistically Estimated) Reputations - HIPER Method splits user reputation into two related values: Hidden Reputation (HR) is directly calculated from a set of feedbacks, Indirect Reputation (IR) is a probabilistically estimated projection of the hidden reputation and its value is public. Such indirect connection between received feedbacks and a visible reputation value allows users to provide unbiased feedbacks without fear of retaliation.
Rocznik
Strony
35--51
Opis fizyczny
Bibliogr. 22 poz., rys.
Twórcy
  • Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland
  • Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland
Bibliografia
  • [1] Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy computational models for trust and reputation systems. Electron. Commer. Rec. Appl. 8(1), 37-47 (2009)
  • [2] Bhattacharjee, R.: Avoiding ballot stuffing in ebay-like reputation systems. third workshop on economics of peer-to-peer systems. In: In: P2PECON 05: Proceeding of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems. pp. 133-137. ACM Press (2005)
  • [3] Dellarocas, C., Wood, C.A.: The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias. Management Science 54, 460-476 (2008)
  • [4] Huynh, T.D., Jennings, N.R., Shadbolt, N.: FIRE: An Integrated Trust and Reputation Model for Open Multi-Agent Systems. In: 16th European Conference on Artificial Intelligence. pp. 18-22 (2004), event Dates: 2004
  • [5] Jøsang, A., Golbeck, J.: Challenges for robust trust and reputation systems. In: Proceedings of the 5th International Workshop on Security and Trust Management (SMT 2009), Saint Malo, France (2009)
  • [6] Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decision Support Systems 43(2), 618 - 644 (2007), http:// www.sciencedirect.com/science/article/pii/S0167923605000849, emerging Issues in Collaborative Commerce
  • [7] Kaszuba, T., Hupa, A., Wierzbicki, A.: Advanced Feedback Management for Internet Auction Reputation Systems. IEEE Internet Computing 14, 31-37 (2010)
  • [8] Kaszuba, T., Turek, P., Wierzbicki, A., Nielek, R.: Prototrust: An environment for improved trust management in internet auctions. In: Local Proceedings of 13th East-European Conference, ADBIS 2009. pp. 385-398. JUMI Pubbbblishing House Ltd. (2009)
  • [9] Klein, T.J., Lambertz, C., Spagnolo, G., Stahl, K.O.: Last Minute Feedback. Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 62, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich (Mar 2006), http://ideas.repec.org/pZtrf/wpaper/62.html
  • [10] Kwan, M.Y.K., Overill, R.E., Chow, K.P., Silomon, J.A.M., Tse, H., Law, F.Y.W., Lai, P.K.Y.: Evaluation of Evidence in Internet Auction Fraud Investigations. In: IFIP Int. Conf. Digital Forensics. pp. 121-132 (2010)
  • [11] Leszczyński, K., Zakrzewicz, M.: Asymptotic Trust Algorithm: Extension for Reputation Systems in Online Auctions. Control and Cybernetics 40(3), 651666 (2011)
  • [12] Morzy, M.: New Algorithms for Mining the Reputation of Participants of Online Auctions. In: WINE. pp. 112-121 (2005)
  • [13] Morzy, M., Wierzbicki, A.: The Sound of Silence: Mining Implicit Feedbacks to Compute Reputation. In: WINE. pp. 365-376 (2006)
  • [14] O’Donovan, J., Evrim, V., Smyth, B., McLeod, D., Nixon, P.: Personalizing Trust in Online Auctions. In: STAIRS. pp. 72-83 (2006)
  • [15] Reichling, F.: Effects of reputation mechanisms on fraud prevention in ebay auctions. Tech. rep., Working Paper, Stanford University (2004)
  • [16] Resnick, P., Zeckhauser, R.: Trust Among Strangers in Internet Transactions: Empirical Analysis of eBays Reputation System. The Economics of the Internet and E-Commerce 11(2), 23-25 (2002)
  • [17] Zacharia, G., Maes, P.: Trust management through reputation mechanisms. Applied Artificial Intelligence 14(7), 881-907 (2000)
  • [18] Zhang, H., Duan, H.X., Liu, W.: RRM: An incentive reputation model for promoting good behaviors in distributed systems. Science in China Series F: Information Sciences 51(11), 1871-1882 (2008)
  • [19] eBay buyer sued for defamation after leaving negative feedback on auction site. http://www.dailymail.co.uk/news/article-1265490/eBay-buyer-sued-defamation-leaving-negative-feedback-auction-site. html
  • [20] ”Klamstwa nie podarujemy” - historia szantazu i negatywnych komentarzy na Allegro. http://technologie.gazeta.pl/internet/2029020,104530,10466941.html
  • [21] Orange County man sued over negative eBay feedback. http://www.wftv.com/news/news/local/orange-county-man-sued-over-negative-ebay-feedback/nPCqn/
  • [22] Buyer sued for eBay feedback. http://news.cnet.com/8301-17852_3-10074157-71.html, http://www.geek.com/articles/news/ebay-negative-feedback-leads-to-lawsuit-20081027/
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c09f50d7-ed15-4811-8606-c87df77a9e46
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