Laure Soulier
Chirag Shah
ABSTRACT
Most of the previous approaches surrounding collaborative information retrieval (CIR) provide either a user-based mediation, in which the system only supports users' collaborative activities, or a system-based mediation, in which the system plays an active part in balancing user roles, re-ranking results, and distributing them to optimize overall retrieval performance. In this paper, we propose to combine both of these approaches by a role mining methodology that learns from users' actions about the retrieval strategy they adapt. This hybrid method aims at showing how users are different and how to use these differences for suggesting roles. The core of the method is expressed as an algorithm that (1) monitors users' actions in a CIR setting; (2) discovers differences among the collaborators along certain dimensions; and (3) suggests appropriate roles to make the most out of individual skills and optimize IR performance. Our approach is empirically evaluated and relies on two different laboratory studies involving 70 pairs of users. Our experiments show promising results that highlight how role mining could optimize the collaboration within a search session. The contributions of this work include a new algorithm for mining user roles in collaborative IR, an evaluation methodology, and a new approach to improve IR performance with the operationalization of user-driven system-mediated collaboration.
- W. K. Edwards. Policies and roles in collaborative applications. In ACM CSCW, pages 11--20, 1996. Google Scholar
Digital Library
- H. Feild and J. Allan. Task-aware query recommendation. In ACM SIGIR, pages 83--92, 2013. Google ScholarDigital Library
- C. Foley and A. F. Smeaton. Synchronous collaborative information retrieval: Techniques and evaluation. In ECIR, pages 42--53, 2009. Google ScholarDigital Library
- J. Freyne, B. Smyth, M. Coyle, E. Balfe, and P. Briggs. Further Experiments on Collaborative Ranking in Community-Based Web Search. Artificial Intelligence Review, 21:229--252, 2004. Google ScholarDigital Library
- S. A. Golder and J. Donath. Social roles in electronic communities. In AoIR, 2004.Google Scholar
- G. Golovchinsky, O. Qvarfordt, and J. Pickens. Collaborative information seeking. IEEE Computer, 42(3):47--51, 2009. Google ScholarDigital Library
- K. Henderson, B. Gallagher, T. Eliassi-Rad, H. Tong, S. Basu, L. Akoglu, D. Koutra, C. Faloutsos, and L. Li. Rolx: Structural role extraction & mining in large graphs. In ACM SIGKDD, pages 1231--1239, 2012. Google ScholarDigital Library
- J. Hyldegard. Collaborative information behaviour - exploring Kuhlthau's Information Search Process model in a group-based educational setting. Information Processing & Management, 42:276--298, 2006. Google ScholarDigital Library
- J. Hyldegard. Beyond the search process - exploring group members' information behavior in context. Information Processing & Management, 45(1):142--158, 2009. Google ScholarDigital Library
- H. Joho, D. Hannah, and J. M. Jose. Revisiting ir techniques for collaborative search strategies. In ECIR, pages 66--77, 2009. Google ScholarDigital Library
- C. C. Kuhlthau. Keynote address: Towards collaboration between information seeking research and information retrieval. Information Research, 10(2), 2005.Google Scholar
- H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In ACM WWW, pages 591--600, 2010. Google ScholarDigital Library
- Y. Lv and C. Zhai. Positional relevance model for pseudo-relevance feedback. In ACM SIGIR, pages 579--586, 2010. Google ScholarDigital Library
- A. McCallum, X. Wang, and A. Corrada-Emmanuel. Topic and role discovery in social networks with experiments on enron and academic email. Journal of Artificial Intelligence Research, 30(1):249--272, 2007. Google ScholarDigital Library
- M. R. Morris and E. Horvitz. Searchtogether: an interface for collaborative web search. In UIST, pages 3--12, 2007. Google ScholarDigital Library
- M. R. Morris, A. Paepcke, and T. Winograd. Teamsearch: Comparing techniques for co-present collaborative search of digital media. In Tabletop 2006, 2006. Google ScholarDigital Library
- M. R. Morris, J. Teevan, and S. Bush. Enhancing collaborative web search with personalization: groupization, smart splitting, and group hit-highlighting. In CSCW, pages 481--484, 2008. Google ScholarDigital Library
- K. Nowicki and T. A. Snijders. Estimation and prediction for stochastic blockstructure. Journal of the American Statistical Association, 96(455):1077--1087, 2001.Google ScholarCross Ref
- A. Pal and S. Counts. Identifying topical authorities in microblogs. In ACM WSDM, pages 45--54, 2011. Google ScholarDigital Library
- J. Pickens, G. Golovchinsky, C. Shah, P. Qvarfordt, and M. Back. Algorithmic mediation for collaborative exploratory search. In ACM SIGIR, pages 315--322, 2008. Google ScholarDigital Library
- T. Rodden. A Survey of CSCW Systems. Interacting with Computers, 3(3):319--353, 1991.Google ScholarCross Ref
- C. Shah and R. González-Ibánez. Evaluating the synergic effect of collaboration in information seeking. In ACM SIGIR, pages 913--922, 2011. Google ScholarDigital Library
- C. Shah and R. Gonzalez-Ibanez. Coagmento - a system for supporting collaborative information seeking. In ASIST, pages 9--12, 2011.Google Scholar
- C. Shah, J. Pickens, and G. Golovchinsky. Role-based results redistribution for collaborative information retrieval. Information Processing & Management, 46(6):773--781, 2010. Google ScholarDigital Library
- B. Smyth, E. Balfe, P. Briggs, M. Coyle, and J. Freyne. Collaborative Web Search. In IJCAI, pages 1417--1419, Acapulco, Mexico, 2003. Google ScholarDigital Library
- L. Soulier, L. Tamine, and W. Bahsoun. A collaborative document ranking model for a multi-faceted search. In Springer AIRS, pages 109--120, 2013.Google ScholarCross Ref
- L. Soulier, L. Tamine, and W. Bahsoun. On domain expertise-based roles in collaborative information retrieval. Information Processing & Management, 2014, to appear.Google ScholarDigital Library
- S. Talja. Information sharing in academic communities: Types and levels of collaboration in information seeking and use. New Review of Information Behaviour Research, 3:143--160, 2002.Google Scholar
- J. Teevan, S. T. Dumais, and E. Horvitz. Personalizing search via automated analysis of interests and activities. In ACM SIGIR, pages 449--456, 2005. Google ScholarDigital Library
- J. Xu and W. B. Croft. Query expansion using local and global document analysis. In ACM SIGIR, pages 4--11, 1996. Google ScholarDigital Library
Index Terms
- User-driven system-mediated collaborative information retrieval
Recommendations
Collaborative Information Retrieval: Frameworks, Theoretical Models, and Emerging Topics
ICTIR '16: Proceedings of the 2016 ACM International Conference on the Theory of Information RetrievalA great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-...
Contextual Support for Collaborative Information Retrieval
CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and RetrievalRecent research shows that Collaborative Information Retrieval (CIR), in which two or more users collaborate on the same search task, has become increasingly popular. The presence of both search and collaboration behaviors makes CIR a complex search ...
Understanding the Impact of the Role Factor in Collaborative Information Retrieval
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge ManagementCollaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the ...
Comments