Identifying web search session patterns using cluster analysis: A comparison of three search environments

Journal of the American Society for Information Science and Technology

Volumn 60, Issue 5, 2009, Pages 896-910

  Wolfram, Dietmar ^a   Wang, Peiling ^b   Zhang, Jin ^a  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^b UNIVERSITY OF TENNESSEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSUMER HEALTH INFORMATION; CONTEXT SENSITIVE; DATA SETS; HELP SYSTEMS; QUERY MODIFICATION; SEARCH BEHAVIOR; TRANSACTION LOG; WEB SEARCHES;

CLUSTER ANALYSIS; INFORMATION RETRIEVAL; SEARCH ENGINES;

WEBSITES;

EID: 65649089787     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.21034     Document Type: Article

References (42)

1. Aldenderfer, M.S., & Blashfield, R.K. (1984). Cluster analysis. Beverly Hills: Sage Publications.
2. Baeza-Yates, R., Hurtado, C., Mendoza, M., & Dupret, G. (2005). Modeling user search behavior. In Proceedingof the Third Latin American Web Congress (pp. 242-251).Washington: IEEE Computer Society.
3. Beeferman, D., & Berger, A. (2000). Agglomerative clustering of a search engine query log. In Proceedings of the SixthACMSIGKDD internaConference on Knowledge Discovery and Data Mining (pp. 407-416) NewYork: ACM
4. Beitzel, S.M., Jensen, E.C., Chowdhury, A., Frieder, O., & Grossman, D (2007). Temporal analysis of a very large topically categorized query log. Journal of the American Society for Information Science and Technology, 58(2), 166-178.
5. Blecic, D.D., Dorsch, J.L., Koenig, M.H., & Bangalore, N.S. (1999). A longitudinal study of the effects of OPAC screen changes. College and Research Libraries, 60(6), 515-530.
6. Chen, H-M., & Cooper, M.D. (2001). Using clustering techniques to detect usage patterns in a Web-based information system. Journal of the American Society for Information Science and Technology, 52(11), 888-904.
7. Cooper, M.D. (2001). Usage patterns of aWeb-based library catalog. Journal of the American Society for Information Science and Technology, 52(2), 137-148
8. Cooper, M.D., & Chen, H-M. (2001). Predicting the relevance of a library catalog search. Journal of the American Society for Information Science and Technology, 52(10), 813-827.
9. Göker, A.,&He, D. (2002). AnalysingWeb search logs to determine session boundaries for user-oriented learning. Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 319-322). London: Springer-Verlag.
10. He, D., Göker, A.,&Harper, D.J. (2002). Combining evidence for automatic Web session identification. Information Processing&Management, 38(5), 727-742.
11. Hoelscher, C. (1998). How Internet experts search for information on the Web. In H. Maurer & R.G. Olson (Eds.), Proceedings ofWebNet98World Conference of theWWW, Internet & Intranet. Charlottesville, VA
12. Hu, J., Zeng, H.J., Li, H., Niu, C., & Chen, Z. (2007). Demographic prediction based on user's browsing behavior. In WWW '07 Proceedings of the 16th International Conference on World Wide Web (pp. 151-160) NewYork: ACM
13. Huang, X., Peng, F., An, A., & S, D. (2004). Dynamic Web log session identification with statistical language models. Journal of the American Society for Information Science and Technology. 55(14), 1290-1303
14. Huang, Z., Ng, J., Cheung, D.W., Ng, M.K., & Ching,W.K. (2001). A cube model and cluster analysis for Web access sessions. In Proceedings of WEBKDD 2001, pp. 47-57
15. Jansen, B.J., & Spink, A. (2005). How are we searching the World Wide Web?: An analysis of nine search engine transaction logs. Information Processing & Management. 42(1), 248-263.
16. Jansen, B.J., Spink, A., & Saracevic, T. (2000). Rlife, real users, and real needs: A study and analysis of user queries on theWeb. Information Processing & Management, 36(2), 207-227.
17. Jansen, B.J., Spink,A., Blakely, C.,&Koshman, S. (2007). Defining a session search engines. Journal of the American Society for Information Science and Technology, 58(6), 862-871
18. Montgomery, A.L., & Faloutsos, C. (2000). Trends and patterns of WWW browsing behavior.Retrieved December 10, 2008, from http://pages.cpsc.ucalgary. ca/?/personal/other-pubs/web-trends.pdf.
19. Murray, G.C., Lin, A., & Chowdhury, A. (2006). Identification of user sessions with hierarchical agglomerative cluste. In Proceedings of the ASIS&T Annual Meeting [CD-ROM]. Medford, NJ: Information Today, Inc.
20. Novak, J.D. (1998). Learning, creating, and using knowledge. Mahwah, NJ: Lawrence Erlbaum
21. Nowick, E.A., Eskridge, K.M., Travnicek, D.A., Chen, X., & Li, J. (2005) A model search engine based on cluster analysis of user search terms Library Philosophy and Practice, 7(2). Retrieved December 10, 2008, from http://www.webpages.u.edu/?mbolin/nowick-et-al.htm
22. Özmutlu, H.C., & çavdur, F. (2005). Application of automatic topic identification on ExciteWeb search engine data logs. Information Processing & Management, 41, 1243-1262.
23. Pass, G., Chowdhury, A., & Torgeson, C. (2006). A picture of search Retrieved December 10, 2008, from http://ir.iit.edu/~abdur/publications/pos- infoscale.pdf
24. Ross, N.C.M.,&Wolfram, D. (2000). End-user searchon the Internet:An analysis of term pair topics submitted to the Excite search engine. Journal of the American Society for Information Science, 51(10), 949-958.
25. Sadagopan, N., & Li, J. (2008). Characterizing typical and atypical user sessions in Clickstreams. InWWW'08 Proceedings of the 17th International Conference onWorldWideWeb (pp. 885-894). NewYork: ACM.
26. Shaaban, S., McKechnie, J., & Lockley, S. (2003). Modelling information behaviour of AEC professionals on online technical information resources. ITcon, 8, 265-281. Retrieved December 10, 2008, from http://www.itcon.org/data/works/att/2003-20.content.03818.pdf.
27. Silverstein, C., Marais, H., Henzinger, M., & Moricz, M. (1999). Analysis of a very large Web search engine query log. SIGIR Forum 33, 1 (Sep. 1999), 6-12.
28. SPSS. (2001). The SPSS TwoStep Cluster Component. Retrieved December 10, 2008, from ftp://ftp.spss.com/pub/web/wp/TSCWP-0101.pdf.
29. Spink, A, & Jansen, B.J. (2004). Web search: Public searching of the Web Dordrecht: Kluwer.
30. Spink, A., Jansen, B.J., Wolfram, D., & Saracevic, T. (2002). From e-sex to e-commerce: Web search changes. Computer Magazine, 35(3), 107-109.
31. Spink, A., Wolfram, D. & Jans, B.J., & Saracevic, T. (2001). Searching the Web: The public and their queries. Journal of the American Society for Information Science and Technology, 52(3), 226-234.
32. Theodoridis, S., & K. Koutroumbas. (1999). Pattern recognition. NewYork: Academic Press
33. Wang, P. (2006). Final Report on ALISE/OCLC 2005 Research Grant: A Dual-approach to Web query mining: Towards conceptual representations of information needs. Retrieved December 10, 2008, from http:// www.oclc.org/programsandresearch/grants/reports/2005/wang-p.pdf
34. Wang, P., Berry, M.W., &Yang,Y. (2003). Mining longitudinalWeb queries: Trends and patterns. Journal of the American Society for
35. Wang, P., Wolfram, D., Zhang, J., Hong, N., Wu, L., Canevit, C., & Redmon, D. (2007). Mining Web search behaviors: Strategies and techniques fdata modeling and analysis. In Proceedings of the 2007 Annual Meeting American Society for Information Science and Technology
36. Wen, J., Nie, J., & Zhang, H. (2001). Clustering user queries of a search engine. In Proof the 10th International Conference on World Wide Web. Retrieved January 16, 2009, from http://www10.org/cdrom/ papers/368/index.html
37. Wolfram, D. (1999). Term co-occurrence in Internet search engine queries: An of the Excite data set. Canadian Journal of Information and Library Science, 24(2/3), 12-33.
38. Wolfram, D. (2000). A query-level examination of end user searching behaviour on the excite search eng. In H. Olson, (Ed.). Proceedings of the 28th Annual Conference of the Canadian Association for Information Science. Retrieved December 10, 2008, from http://www.caisacsi ca/proceedings/2000/wolfram-2000. pdf.
39. Wolfram, D. (2008). Search characteristics in different types of Webbased IR environments: Are they the same? Information Processing & Management, 44, 1279-1292.
40. Wolfram, D., Wang, P., & Zhang, J. (2007). Modeling Web session using cluster analysis: A comparison of three search settings. In Proceedings of ASIST 2007 Annual Meeting (Milwaukee, WI, October 19-24)
41. Wolfram, D., & Xie, H. (2000). End user database searching over the Internet: An analysis of the state of Wisconsin'BadgerLink service. In M. E. Williams (Ed.), Proceedings of the 21st National Online Meeting (pp. 503-512). Medford, NJ: Information Today.
42. Zhang, J., Wolfram, D., Wang, P., Hong, Y., & Gillis, R. (2008). Visualization of health subject analysis based on query term co-occurrences. Journal of the American Society for Information Science & Technology, 59(12), 1933-1947.