Extracting Reputation with Knock-Out Tournament-Based Pairwise Elicitation in Complex Social Networks

  • Roberto Centeno
  • Ramón Hermoso
  • Maria Fasli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8068)


Complex social networks are typically used in order to represent and structure social relationships that do not follow a predictable pattern of behaviour. Due to their openness and dynamics, participants in these networks have to constantly deal with uncertainty prior to commencing in any type of interaction. Reputation appears as a key concept helping users to mitigate such uncertainty. However, most of the reputation mechanisms proposed in the literature suffer from problems such as the subjectivity in the opinions and their inaccurate aggregation. With these problems in mind, this paper presents a decentralized reputation mechanism based on the concepts of pairwise elicitation processes and knock-out tournaments. The main objective of this mechanism is to build reputation rankings from qualitative opinions, so getting rid of the subjectivity problems associated with the aggregation of quantitative opinions. The proposed approach is evaluated in a real environment by using a dataset extracted from MovieLens.


Ground Truth Reputation System Reputation Mechanism Subjectivity Problem Discount Cumulative Gain 
These 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.


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  1. 1.
    Altman, A., Tennenholtz, M.: Ranking systems: the pagerank axioms. In: Proceedings of the 6th ACM Conference on Electronic Commerce, EC 2005, pp. 1–8. ACM, New York (2005)Google Scholar
  2. 2.
    Balakrishnan, S., Chopra, S.: Two of a kind or the ratings game? adaptive pairwise preferences and latent factor models. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pp. 725–730. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  3. 3.
    Brams, S.J., Fishburn, P.C.: Voting procedures. In: Arrow, K.J., Sen, A.K., Suzumura, K. (eds.) Handbook of Social Choice and Welfare, vol. 1, ch.4, pp. 173–236. Elsevier (2002)Google Scholar
  4. 4.
    Conitzer, V.: Eliciting single-peaked preferences using comparison queries. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2007, pp. 65:1–65:8. ACM, New York (2007)Google Scholar
  5. 5.
    Conitzer, V., Rognlie, M., Xia, L.: Preference functions that score rankings and maximum likelihood estimation. In: Proceedings of the 21st International Joint Conference on Artifical Intelligence, IJCAI 2009, pp. 109–115. Morgan Kaufmann Publishers Inc., San Francisco (2009)Google Scholar
  6. 6.
    Fu, F., Hauert, C., Nowak, M.A., Wang, L.: Reputation-based partner choice promotes cooperation in social networks. Phys. Rev. E 78, 026117 (2008)Google Scholar
  7. 7.
    Fullam, K.K., Barber, K.S.: Dynamically learning sources of trust information: experience vs. reputation. In: Proceedings of AAMAS 2007, pp. 1055–1062. ACM, New York (2007)Google Scholar
  8. 8.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRefGoogle Scholar
  9. 9.
    Negahban, S., Oh, S., Shah, D.: Iterative ranking from pair-wise comparisons. CoRR, abs/1209.1688 (2012)Google Scholar
  10. 10.
    Pujol, J.M., Sangüesa, R., Delgado, J.: Extracting reputation in multi agent systems by means of social network topology. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, AAMAS 2002, pp. 467–474. ACM, New York (2002), http://doi.acm.org/10.1145/544741.544853 CrossRefGoogle Scholar
  11. 11.
    Raub, W., Weesie, J.: Reputation and efficiency in social interactions: An example of network effects. American Journal of Sociology 96, 626–654 (1990)CrossRefGoogle Scholar
  12. 12.
    Resnick, P., Kuwabara, K., Zeckhauser, R., Friedman, E.: Reputation systems. Commun. ACM 43(12), 45–48 (2000)CrossRefGoogle Scholar
  13. 13.
    Vega-Redondo, F.: Complex Social Networks. Cambridge University Press (2007)Google Scholar
  14. 14.
    Vu, T., Altman, A., Shoham, Y.: On the complexity of schedule control problems for knockout tournaments. In: Proceedings of AAMAS 2009, vol. 1, pp. 225–232. IFAAMAS, Richland (2009)Google Scholar
  15. 15.
    Vu, T., Shoham, Y.: Fair seeding in knockout tournaments. ACM Trans. Intell. Syst. Technol. 3(1), 9:1–9:17 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roberto Centeno
    • 1
  • Ramón Hermoso
    • 2
  • Maria Fasli
    • 2
  1. 1.Dpto. de Lenguajes y Sistemas InformáticosUNEDSpain
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexUK

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