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Collaborative Information Retrieval: Concepts, Models and Evaluation

  • Lynda Tamine
  • Laure SoulierEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

Recent work have shown the potential of collaboration for solving complex or exploratory search tasks allowing to achieve synergic effects with respect to individual search, which is the prevalent information retrieval (IR) setting this last decade. This interactive multi-user context gives rise to several challenges in IR. One main challenge relies on the adaptation of IR techniques or models [8] in order to build algorithmic supports of collaboration distributing documents among users. The second challenge is related to the design of Collaborative Information Retrieval (CIR) models and their effectiveness evaluation since individual IR frameworks and measures do not totally fit with the collaboration paradigms. In this tutorial, we address the second challenge and present first a general overview of collaborative search introducing the main underlying notions. Then, we focus on related work dealing with collaborative ranking models and their effectiveness evaluation. Our primary objective is to introduce these notions by highlighting how and why they should be different from individual IR in order to give participants the main clues for investigating new research directions in this domain with a deep understanding of current CIR work.

Keywords

Collaborative information retrieval Collaboration Search process Ranking model Evaluation 

References

  1. 1.
    Azzopardi, L., Pickens, J., Sakai, T., Soulier, L., Tamine, L.: Ecol: first international workshop on the evaluation on collaborative information seeking and retrieval. In: CIKM 2015, pp. 1943–1944 (2015)Google Scholar
  2. 2.
    Capra, R., Velasco-Martin, J., Sams, B.: Levels of “working together” in collaborative information seeking and sharing. In: CSCW 2010, ACM (2010)Google Scholar
  3. 3.
    Evans, B.M., Chi, E.H.: An elaborated model of social search. Inf. Process. Manag. (IP&M) 46(6), 656–678 (2010)CrossRefGoogle Scholar
  4. 4.
    Foley, C., Smeaton, A.F.: Synchronous collaborative information retrieval: techniques and evaluation. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 42–53. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Golovchinsky, G., Qvarfordt, P., Pickens, J.: Collaborative information seeking. IEEE Comput. 42(3), 47–51 (2009)CrossRefGoogle Scholar
  6. 6.
    Gray, B.: Collaborating: Finding Common Ground for Multiparty Problems. Jossey Bass Business and Management Series. Jossey-Bass, San Francisco (1989)Google Scholar
  7. 7.
    Hyldegärd, J.: Beyond the search process - exploring group members’ information behavior in context. IP&M 45(1), 142–158 (2009)Google Scholar
  8. 8.
    Joho, H., Hannah, D., Jose, J.M.: Revisiting IR techniques for collaborative search strategies. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 66–77. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Kelly, R., Payne, S.J.: Division of labour in collaborative information seeking: current approaches and future directions. In: CIS Workshop at CSCW 2013, ACM (2013)Google Scholar
  10. 10.
    Morris, M.R.: Collaborative search revisited. In: CSCW 2013, pp. 1181–1192. ACM (2013)Google Scholar
  11. 11.
    Morris, M.R., Teevan, J., Bush, S.: Collaborative web search with personalization: groupization, smart splitting, and group hit-highlighting. In: CSCW 2008, pp. 481–484. ACM (2008)Google Scholar
  12. 12.
    Pickens, J., Golovchinsky, G., Shah, C., Qvarfordt, P., Back, M.: Algorithmic mediation for collaborative exploratory search. In: SIGIR 2008, pp. 315–322. ACM (2008)Google Scholar
  13. 13.
    Shah, C.: Collaborative Information Seeking - The Art and Science of Making the Whole Greater than the Sum of All. pp. I–XXI, 1–185. Springer, Heidelberg (2012)Google Scholar
  14. 14.
    Shah, C., González-Ibáñez, R.: Evaluating the synergic effect of collaboration in information seeking. In: SIGIR 2011, pp. 913–922. ACM (2011)Google Scholar
  15. 15.
    Shah, C., Pickens, J., Golovchinsky, G.: Role-based results redistribution for collaborative information retrieval. Inf. Process. Manag. (IP&M) 46(6), 773–781 (2010)CrossRefGoogle Scholar
  16. 16.
    Soulier, L., Shah, C., Tamine, L.: User-driven system-mediated collaborative information retrieval. In: SIGIR 2014, pp. 485–494. ACM (2014)Google Scholar
  17. 17.
    Soulier, L., Tamine, L., Bahsoun, W.: On domain expertise-based roles in collaborative information retrieval. Inf. Process. Manag. (IP&M) 50(5), 752–774 (2014)CrossRefGoogle Scholar
  18. 18.
    Spence, P.R., Reddy, M.C., Hall, R.: A survey of collaborative information seeking practices of academic researchers. In: SIGGROUP Conference on Supporting Group Work, GROUP 2005, pp. 85–88. ACM (2005)Google Scholar
  19. 19.
    Tamine, L., Soulier, L.: Understanding the impact of the role factor in collaborative information retrieval. In: CIKM 2015, ACM, October 2015Google Scholar
  20. 20.
    Twidale, M.B., Nichols, D.M., Paice, C.D.: Browsing is a collaborative process. Inf. Process. Manag. (IP&M) 33(6), 761–783 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Toulouse UPS, IRITToulouse Cedex 9France
  2. 2.Sorbonne Universités, UPMC University of Paris 06ParisFrance
  3. 3.CNRSParisFrance

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