Ryen W. White
Dan Morris
ABSTRACT
One way to help all users of commercial Web search engines be more successful in their searches is to better understand what those users with greater search expertise are doing, and use this knowledge to benefit everyone. In this paper we study the interaction logs of advanced search engine users (and those not so advanced) to better understand how these user groups search. The results show that there are marked differences in the queries, result clicks, post-query browsing, and search success of users we classify as advanced (based on their use of query operators), relative to those classified as non-advanced. Our findings have implications for how advanced users should be supported during their searches, and how their interactions could be used to help searchers of all experience levels find more relevant information and learn improved searching strategies.
- Anick, P. (2003). Using terminological feedback for Web search refinement: A log-based study. In Proc. ACM SIGIR, pp. 88--95. Google Scholar
Digital Library
- Bates, M. (1990). Where should the person stop and the information search interface start? Inf. Proc. Manage. 26, 5, 575--591. Google ScholarDigital Library
- Belkin, N. J. (2000). Helping people find what they don't know. Comm. ACM, 43, 8, 58--61. Google ScholarDigital Library
- Belkin, N. J. et al. (2003). Query length in interactive information retrieval. In Proc. ACM SIGIR, pp. 205--212. Google ScholarDigital Library
- Bhavnani, S. K. (2001). Domain-specific search strategies for the effective retrieval of healthcare and shopping information. In Proc. ACM SIGCHI, pp. 610--611. Google ScholarDigital Library
- Chi, E. H., Pirolli, P. L., Chen, K. & Pitkow, J. E. (2001). Using information scent to model user information needs and actions and the Web. In Proc. ACM SIGCHI, pp. 490--497. Google ScholarDigital Library
- De Lima, E. F. & Pedersen, J. O. (1999). Phrase recognition and expansion for short, precision-biased queries based on a query log. In Proc. of ACM SIGIR, pp. 145--152. Google ScholarDigital Library
- Eastman, C. M. & Jansen, B. J. (2003). Coverage, relevance, and ranking: The impact of query operators on Web search engine results. ACM TOIS, 21, 4, 383--411. Google ScholarDigital Library
- Efthimiadis, E. N. (1996). Query expansion. Annual Review of Information Science and Technology, 31, 121--187.Google Scholar
- Furnas, G. (1985). Experience with an adaptive indexing scheme. In Proc. ACM SIGCHI, pp. 131--135. Google ScholarDigital Library
- Furnas, G. W., Landauer, T. K., Gomez, L. M. & Dumais, S. T. (1987). The vocabulary problem in human-system communication: An analysis and a solution. Comm. ACM, 30, 11, 964--971. Google ScholarDigital Library
- Granka, L., Joachims, T. & Gay, G. (2004). Eye-tracking analysis of user behavior in WWW search. In Proc. ACM SIGIR, pp. 478--479. Google ScholarDigital Library
- Hölscher, C. & Strube, G. (2000). Web search behavior of internet experts and newbies. In Proc.WWW, pp. 337--346. Google ScholarDigital Library
- Jansen, B. J. (2000). An investigation into the use of simple queries on Web IR systems. Inf. Res. 6, 1.Google Scholar
- Jansen, B. J., Spink, A. & Saracevic, T. (2000). Real life, real users, and real needs: A study and analysis of user queries on the Web. Inf. Proc. Manage. 36, 2, 207--227. Google ScholarDigital Library
- Jones, R., Rey, B., Madani, O. & Greiner, W. (2006). Generating query substitutions. In Proc. WWW, pp. 387--396. Google ScholarDigital Library
- Kaski, S., Myllymäki, P. & Kojo, I. (2005). User models from implicit feedback for proactive information retrieval. In Workshop at UM Conference; Machine Learning for User Modeling: Challenges.Google Scholar
- Kelly, D., Dollu, V. D. & Fu, X. (2005). The loquacious user: a document-independent source of terms for query expansion. In Proc. ACM SIGIR, pp. 457--464. Google ScholarDigital Library
- Lazonder, A. W., Biemans, H. J. A. & Woperis, I. G. J. H. (2000). Differences between novice and experienced users in searching for information on the World Wide Web. J. ASIST. 51, 6, 576--581. Google ScholarDigital Library
- Morita, M. & Shinoda, Y. (1994). Information filtering based on user behavior analysis and best match text retrieval. In Proc. ACM SIGIR, pp. 272--281. Google ScholarDigital Library
- NIST Special Publication 500--266: The Fourteenth Text Retrieval Conference Proceedings (TREC 2005).Google Scholar
- Oddy, R. (1977). Information retrieval through man-machine dialogue. J. Doc. 33, 1, 1--14.Google ScholarCross Ref
- Rose, D. E. & Levinson, D. (2004). Understanding user goals in Web search. In Proc. WWW, pp. 13--19. Google ScholarDigital Library
- Salton, G. and Buckley, C. (1990). Improving retrieval performance by relevance feedback. J. ASIST, 41 4, 288--287.Google ScholarCross Ref
- Silverstein, C., Marais, H., Henzinger, M. & Moricz, M. (1999). Analysis of a very large web search engine query log. SIGIR Forum, 33, 1, 6--12. Google ScholarDigital Library
- Spearman, C. (1904). "General intelligence," objectively determined and measured. Amer. J. Psy. 15, 201--293.Google ScholarCross Ref
- Spink, A., Bateman, J. & Jansen, B. J. (1998). Searching heterogeneous collections on the Web: Behavior of Excite users. Inf. Res. 4, 2, 317--328.Google Scholar
- Spink, A., Griesdorf, H. & Bateman, J. (1998). From highly relevant to not relevant: examining different regions of relevance. Inf. Proc. Manage. 34 5, 599--621. Google ScholarDigital Library
- Teevan, J. et al. (2004). The perfect search engine is not enough: A study of orienteering behavior in directed search. In Proc. ACM SIGCHI, pp. 415--422. Google ScholarDigital Library
- Teevan, J., Dumais, S. T. & Horvitz, E. (2005). Personalizing search via automated analysis of interests and activities. In Proc. ACM SIGIR, pp. 449--456 Google ScholarDigital Library
- Wang, P., Berry, M. & Yang, Y. (2003). Mining longitudinal Web queries: Trends and patterns. J. ASIST, 54, 3, 742--758. Google ScholarDigital Library
- White, R. W., Bilenko, M. & Cucerzan, S. (2007). Studying the use of popular destinations to enhance Web search interaction. In Proc. ACM SIGIR, in press. Google ScholarDigital Library
- White, R. W. & Drucker, S. (2007). Investigating behavioral variability in Web search. In Proc. WWW, in press. Google ScholarDigital Library
- White, R. W., Ruthven, I. & Jose, J. M. (2002). Finding relevant documents using top-ranking sentences: An evaluation of two alternative schemes. In Proc. ACM SIGIR, pp. 57--64. Google ScholarDigital Library
- Wildemuth, B. M., do Bleik, R., Friedman, C. P. & File, D. D. (1995). Medical students' personal knowledge. Search proficiency, and database use in problem solving. J. ASIST, 46, 590--607. Google ScholarDigital Library
Index Terms
- Investigating the querying and browsing behavior of advanced search engine users
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