Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time

  • Luca Longo
  • Pierpaolo Dondio
  • Stephen Barrett
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6450)


The Web has been growing in size and with the proliferation of large-scale collaborative computing environments, Social Search has become increasingly important. This recent field focuses on assigning relevance to Web-pages by considering the reader’s perspective rather than Web-masters’ point of view. Current searching technologies of this form tend to rely on explicit human recommendations. In part because it is hard to obtain user feedback, these methods are hard to scale. The challenge is in producing implicit rankings, by reasoning over users’ Web-search activity, without recourse to explicit human intervention. This paper focuses on a novel Social Search model based on Information Foraging Theory, Effort and Computational Trust, showing a different way to implicitly judge Web-entities. The formalism has been divided in two sub-models. The first considers the effort expended by users, in viewing Web-sites, to assess their relevance to a given searching problem. The second enhances the first sub-model by considering only the most trustworthy users’ opinions, identified by Computational Trust techniques.

100 university students explicitly evaluated the usefulness of 12 thematic Web-sites and their browsing activity were gathered. Correlation indexes suggests the existence of a considerable relationship between explicit feedback and implicit computed judgements that were further compared to the ones, provided by the Google search engine. The proposed nature-inspired approach suggests that, by considering the same searching query, Social Search to be more effective than the Google Page-Rank Algorithm. The consideration of the only identified trustworthy students’ implicit feedback provides a way to increase the accuracy of the effort-based approach. This evidence supports the presentation of a schema for a Social Search engine that generates implicit rankings by considering the collective intelligence emerged from users on the Web.


Multiagent System Collective Intelligence Implicit Feedback Forage Theory Explicit Judgement 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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)CrossRefGoogle Scholar
  5. 5.
    Celentani, M., Fudenberg, D., Levine, D.K., Psendorfer, W.: Maitaining a reputation Against a Long-Lived Opponent. Econometria 64(3), 691–704 (1966)CrossRefzbMATHGoogle Scholar
  6. 6.
    Chi Ed H.: Information Seeking Can Be Social. Computer 42(3), 42–46 (2009)Google Scholar
  7. 7.
    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
  8. 8.
    Gambetta, D.: From the book Trust: Making and Breaking Cooperative Relations. In: Can we trust trust?, pp. 213–237 (2000)Google Scholar
  9. 9.
    Golder, S.A., Huberman, B.A.: Usage Patterns of Collaborative Tagging Systems. Journal of Information Science 32(2), 198–208 (2006)CrossRefGoogle Scholar
  10. 10.
    Hume, D.: A Treatise of Human Nature. Clarendon Press, Oxford (1737) (1975)Google Scholar
  11. 11.
    Karlins, M., Abelson, H.I.: Persuasion, how opinion and attitudes are changed. Crosby Lockwood & Son (1970)Google Scholar
  12. 12.
    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
  13. 13.
    Abdi, H.: Kendall Rank Correlation. In: Salkind, N.J. (ed.) Encyclopaedia of Measurement and Statistics, Sage, Thousand Oaks (2007)Google Scholar
  14. 14.
    Kitajima, M., Blackmon, M.H., Polson, P.G.: Cognitive Architecture for Website Design and Usability evaluation: Comprehension and Information Scent in Performing by Exploration. In: HCI, Las Vegas (2005)Google Scholar
  15. 15.
    Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Longo, L., Barrett, S., Dondio, P.: Toward Social Search: from Explicit to Implicit Collaboration to Predict Users’ Interests. In: WebIST 2009 (2009)Google Scholar
  17. 17.
    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
  18. 18.
    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, 5-7 October (2009)Google Scholar
  19. 19.
    Luhmann, N.: Book Trust: Making and Breaking Cooperative Relations. In: Familiarity, confidence, trust: Problems and alternatives, pp. 213–237 (2000)Google Scholar
  20. 20.
    Marsh, S.: Formalizing Trust as Computational Concept. PhD, Stirling (1994)Google Scholar
  21. 21.
    Miller, C.S., Remington, R.W.: Modeling Information Navigation: implications for Information Architecture. In: HCI (2004)Google Scholar
  22. 22.
    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
  23. 23.
    Morita, M., Shinoda, Y.: Information Filtering Based on User Behavior analysis and Best Match Text Retrieval. In: 17th ACM SIGIR (1996)Google Scholar
  24. 24.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Standford University, Standford (1999)Google Scholar
  25. 25.
    Pirolli, P.: Information Foraging Theory. Adaptive Interaction with Information. Oxford University Press, Oxford (2007)CrossRefGoogle Scholar
  26. 26.
    Pirolli, P., Fu, W.: SNIF-ACT: A Model of Information Foraging on the World Wide Web. In: 9th International Conference on, User Modeling 2003 (2003)Google Scholar
  27. 27.
    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
  28. 28.
    Stephens, D.W., Krebs, J.R.: Foraging Theory. Princeton, NJ (1986)Google Scholar
  29. 29.
    Velayathan, G., Yamada, S.: Behavior-based Web Page Evaluation. In: WWW 2007, Banff, Alberta, Canada, May 8-12 (2007)Google Scholar
  30. 30.
    Viégas, B.F., Wattenberg, M., Kushal, D.: Studying Cooperation and Conflict between Authors with history from Visualizations, MIT Media Lab. and IBM ResearchGoogle Scholar
  31. 31.
    Weiss, A.: The Power of Collective Intelligence. Collective Intelligence, 19-23 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Luca Longo
    • 1
  • Pierpaolo Dondio
    • 1
  • Stephen Barrett
    • 1
  1. 1.Department of Computer Science and StatisticsTrinity CollegeDublinIreland

Personalised recommendations