Analysis and evaluation of query reformulations in different task types

Proceedings of the ASIST Annual Meeting

Volumn 47, Issue , 2010, Pages

  Liu, Chang ^a   Gwizdka, Jacek ^a   Liu, Jingjing ^a   Xu, Tao ^b   Belkin, Nicholas J ^a  

^a RUTGERS UNIVERSITY   (United States)

^b State University of New York   (United States)

Author keywords

Query reformulation; Reformulation type; Search effectiveness; Task type

Indexed keywords

EID: 84857374491     PISSN: None     EISSN: 15508390     Source Type: Journal    
DOI: 10.1002/meet.14504701214     Document Type: Conference Paper

References (16)

1. Borlund, P. (2003). The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Information Research, 8(3), paper no. 152 Retrieved May 10, 2010, from http://informationr.net/ir/8-3/paper152. html.
2. Fidel, R. (1985). Moves in online searching. Online Review, 9(1), 61-74.
3. He, D., Göker, A., & Harper, D. J. (2002). Combining evidence for automatic web session identification. Information Processing & Management, 38(5), 727-742.
4. Hembrooke, H. A., Granka, L. A., Gay, G. K., & Liddy, E. D. (2005). The effects of expertise and feedback on search term selection and subsequent learning: Research Articles. Journal of the American Society for Information Science and Technology, 56(8), 861-871.
5. Hsieh-Yee, I. (1993). Effects of search experience and subject knowledge on the search tactics of novice and experienced searchers. Journal of the American Society for Information Science, 44(3), 161-174.
6. Hsieh-Yee, I. (1998). Search Tactics of Web Users in Searching for Texts, Graphics, Known Items and Subjects - A Search Simulation Study. The Reference Librarian, 28(60), 61-85.
7. Huang, J., & Efthimiadis, E. N. (2009). Analyzing and evaluating query reformulation strategies in web search logs. In Proceeding of the 18th ACM conference on Information and knowledge management (pp. 77-86). Hong Kong, China
8. Jansen, B. J., Spink, A., Blakely, C., & Koshman, S. (2007). Defining a session on Web search engines: Research Articles. Journal of the American Society for Information Science and Technology, 58(6), 862-871.
9. Lau, T., & Horvitz, E. (1999). Patterns of search: analyzing and modeling Web query refinement. In Proceedings of the seventh international conference on User modeling (pp.119-128). Banff, Canada.
10. Li, Y. (2008). Relationships among work tasks, search tasks, and interactive information searching behavior. (Unpublished doctoral dissertation, Rutgers University, 2008)
11. Liu, C., Gwizdka, J., & Belkin, N.J. (2010). Analysis of Query Reformulation Types on Different Search Tasks. A poster to be presented at iConference 2010, Urbana-Champaign, IL, February 3-6, 2010.
12. Ozmutlu, S. (2006). Automatic new topic identification using multiple linear regression. Information Processing & Management, 42(4), 934-950.
13. Rieh, S. Y., & Xie, H. (2006). Analysis of multiple query reformulations on the web: The interactive information retrieval context. Information Processing & Management, 42(3), 751-768.
14. Shenouda, W. (1991). Online bibliographic searching: How end users modify their search strategy. Proceedings of the 53rd Meeting of the American Society for Information Science, 27, 111-129.
15. Toms, E. G., O'Brien, H., Mackenzie, T., Jordan, C., Freund, L., Toze, S., et al. (2008). Task Effects on Interactive Search: The Query Factor Focused Access to XML Documents: 6th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2007 Dagstuhl Castle, Germany, December 17-19, 2007. Selected Papers (pp. 359-372): Springer-Verlag.
16. Wildemuth, B. M. (2004). The effects of domain knowledge on search tactic formulation. Journal of the American Society for Information Science and Technology, 55(3), 246-258.