Trust-Based Techniques for Collective Intelligence in Social Search Systems

  • Pierpaolo Dondio
  • Luca Longo

Abstract

A key-issue for the effectiveness of collaborative decision support systems is the problem of the trustworthiness of the entities involved in the process. Trust has been always used by humans as a form of collective intelligence to support effective decision making process. Computational trust models are becoming now a popular technique across many applications such as cloud computing, p2p networks, wikis, e-commerce sites, social network. The chapter provides an overview of the current landscape of computational models of trust and reputation, and it presents an experimental study case in the domain of social search, where we show how trust techniques can be applied to enhance the quality of social search engine predictions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agichtein, E., Brill, E., Dumais, S.: Improving Web Search Ranking by Incorporating User Behavior Information. In: SIGIR 2006, Seattle, USA (2006)Google Scholar
  2. 2.
    Agichtein, E., Zheng, Z.: Identifying Best Bet Web Search Results by Mining Past User Behavior. In: KDD 2006, Philadelphia, Pennsylvaia, USA (2006)Google Scholar
  3. 3.
    Atterer, R., et al.: Knowing the User’s Every Move - User Activity Tracking for Website Usability Evaluation and Implicit Interaction. In: WWW 2006, Edinburgh (May 23-26, 2006)Google Scholar
  4. 4.
    Buskens, V.: The Social Structure of Trust. Social Networks (20), 265–298 (1998)Google Scholar
  5. 5.
    Ball, E., Chadwick, D., Basden, A.: The Implementation of a System for evaluating Trust in a Pki Environment. In: Proceedings of Trust in the Network Economy, Evolaris (2003)Google Scholar
  6. 6.
    Celentani, M., Fudenberg, D., Levine, D.K., Psendorfer, W.: Maitaining a Reputation Against a Long-Lived Opponent. Econometria 64(3), 691–704 (1966)CrossRefGoogle Scholar
  7. 7.
    Chi, E.H.: Information Seeking Can Be Social. Computer 42(3), 42–46 (2009)CrossRefGoogle Scholar
  8. 8.
    Castelfranchi, C., Falcone, R.: Trust is much more than Web Probability. In: 32nd Hawaii Int. Conference (2000)Google Scholar
  9. 9.
    Cahill, V., et al.: Using Trust for Secure Collaboration in Uncertain Environments. IEEE Pervasive Computing Magazine 2(3), Special Issue (July-September 2003)Google Scholar
  10. 10.
    Dondio, P., Barrett, S., Weber, S., Seigneur, J.M.: Extracting Trust from Domain Analysis: a Study on Wikipedia. In: IEEE ATC, Wuhan, China (2006)Google Scholar
  11. 11.
    Dondio, P.: Trust as a Form of Defeasible Reasoning. Phd Thesis, Trinity College DublinGoogle Scholar
  12. 12.
    Ford, N., et al.: Web Search Strategies and Human Individual Differences: Cognitive and Demographic Factors, Internet Attitudes, and Approaches. Journal of Am. Soc. Inf. Sci. Technol. 56, 7 (2005)Google Scholar
  13. 13.
    Gambetta, D.: Can we trust trust? . In: Trust: Making and Breaking Cooperative Relations, pp. 213–237 (2000)Google Scholar
  14. 14.
    Golder, S.A., Huberman, B.A.: Usage Patterns of Collaborative Tagging Systems. Journal of Information Science 32(2), 198–208 (2006)CrossRefGoogle Scholar
  15. 15.
    Golbeck, J.: Trust Networks on the Semantic Web. University of Maryland, USA (2002)Google Scholar
  16. 16.
    Hume, D.: A Treatise of Human Nature. Clarendon Press, Oxford (1737) (1975)Google Scholar
  17. 17.
    Hlscher, C., Strube, G.: Web Search Behavior of Internet Experts and Newbies (2000)Google Scholar
  18. 18.
    Josang, A., Pope, S.: Semantic Constraints for Trust Transitivity. In: 2nd Conference on Conceptual Modelling (2005)Google Scholar
  19. 19.
    Joachims, T.: Optimizing Search Engines Using Clickthrough Data. In: The Proceedings of SIGKDD (2002)Google Scholar
  20. 20.
    Karlins, M., Abelson, H.I.: Persuasion, how Opinion and Attitudes are Changed. Crosby Lockwood & Son (1970)Google Scholar
  21. 21.
    Kelly, D., et al.: Reading Time, Scrolling and Interaction: Exploring Implicit Sources of User Preferences for Relevance Feedback During Interactive Information Retrieval. In: SIGIR 2001, New Orleans, USA (2001)Google Scholar
  22. 22.
    Abdi, H.: Kendall Rank Correlation. In: Salkind, N.J. (ed.) Encyclopaedia of Measurement and Statistics. Sage, Thousand Oaks (2007)Google Scholar
  23. 23.
    Kitajima, M., Blackmon, M.H., Polson, P.G.: Cognitive Architecture for Website Design and Usability evaluation: Comprehension and Information Scent in Performing by Exploration. HCI, Las Vegas (2005)Google Scholar
  24. 24.
    Kleinberg, J.: Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46(5), 604–632 (1999)MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Longo, L., Barrett, S., Dondio, P.: Toward Social Search: from Explicit to Implicit Collaboration to Predict Users’ Interests. In: WebIST 2009 (2009)Google Scholar
  26. 26.
    Longo, L., Dondio, P., Barrett, S.: Temporal Factors to Evaluate Trustworthiness of Virtual Identities. In: IEEE SECOVAL 2007, Third International Workshop on the Value of Security through Collaboration, SECURECOMM 2007, Nice, France (September 2007)Google Scholar
  27. 27.
    Longo, L., Barrett, S.: Cognitive Effort for Multi-Agent Systems. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 55–66. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Longo, L., Dondio, P., Barrett, S.: Information Foraging Theory as a Form Of Collective Intelligence for Social Search. In: 1st International Conference on Computational Collective Intelligence Semantic Web, Social Networks & Multiagent Systems, Wroclaw, Poland, (October 5-7, 2009)Google Scholar
  29. 29.
    Luhmann, N.: Familiarity, Confidence, Trust: Problems and Alternatives. In: Trust: Making and Breaking Cooperative Relations, pp. 213–237 (2000)Google Scholar
  30. 30.
    Marsh, S.: Formalizing Trust as Computational Concept. PhD, Stirling (1994)Google Scholar
  31. 31.
    Miller, C.S., Remington, R.W.: Modeling Information Navigation: implications for Information Architecture. In: HCI (2004)Google Scholar
  32. 32.
    Montaner, M., Lopez, B., De La Rosa, J.: Developing Trust in Recommender Agents. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2002), Bologna, Italy, pp. 304–305 (2002)Google Scholar
  33. 33.
    Morita, M., Shinoda, Y.: Information Filtering Based on User Behavior analysis and Best Match Text Retrieval. In: 17th ACM SIGIR (1996)Google Scholar
  34. 34.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Standford University, Standford (1999)Google Scholar
  35. 35.
    Pirolli, P.: Information Foraging Theory. Adaptive Interaction with Information. Oxford University Press, Oxford (2007)Google Scholar
  36. 36.
    Pirolli, P., Fu, W.: SNIF-ACT: A Model of Information Foraging on the World Wide Web. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  37. 37.
    Quercia, D.: STRUDEL: Supporting Trust in the Establishment of Peering Coalitions. In: ACM SAC 2006, pp. 1870–1874 (2006)Google Scholar
  38. 38.
    Robu, V., Halpin, H., Shepherd, H.: Emergence of Consensus and Shared Vocabularies in Collaborative Tagging Systems. ACM Transactions on the Web (TWeb) 3(4), article 14 (September 2009)Google Scholar
  39. 39.
    Stephens, D.W., Krebs, J.R.: Foraging Theory, Princeton, NJ (1986)Google Scholar
  40. 40.
    Sabater, J., Sierra, C.: REGRET: A reputation Model for Gregarious Societies. In: 4th Workshop on Fraud and Trust in Agent Societies, Montreal, Canada, pp. 61–69 (2001)Google Scholar
  41. 41.
    Velayathan, G., Yamada, S.: Behavior-based Web Page Evaluation. In: WWW 2007, Banff, Alberta, Canada, May 8-12 (2007)Google Scholar
  42. 42.
    Viégas, B.F., Wattenberg, M., Kushal, D.: Studying Cooperation and Conflict between Authors with History from Visualizations, MIT Media Lab. and IBM ResearchGoogle Scholar
  43. 43.
    Weiss, A.: The Power of Collective Intelligence. Collective Intelligence, 19–23 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pierpaolo Dondio
    • 1
  • Luca Longo
    • 1
  1. 1.Department of Computer Science and StatisticsTrinity College DublinIreland

Personalised recommendations