Evaluating the effectiveness of search task trails

WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web

Volumn , Issue , 2012, Pages 489-498

  Liao, Zhen ^a   Song, Yang ^b   He, Li Wei ^c   Huang, Yalou ^a  

^a NANKAI UNIVERSITY   (China)

^b MICROSOFT RESEARCH   (United States)

^c MICROSOFT   (United States)

Author keywords

Log analysis; Search log mining; Task evaluation; Task trail

Indexed keywords

LARGE-SCALE DATASETS; LOG ANALYSIS; LOG MINING; POTENTIAL APPLICATIONS; QUERY SUGGESTION; SEARCH APPLICATION; SEARCH BEHAVIOR; SEARCH TASKS; TASK EVALUATION; TASK IDENTIFICATION; TASK TRAIL; TASK-BASED; TOPIC SIMILARITY; USER INFORMATION NEED; USER SATISFACTION; WEB SEARCH LOGS;

INFORMATION RETRIEVAL; SEARCH ENGINES; WEBSITES;

BEHAVIORAL RESEARCH;

EID: 84860851465     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2187836.2187903     Document Type: Conference Paper

References (36)

1. ODP: http://www.dmoz.org/.
2. Bennett, P., Svore, K. and Dumais, S. Classification-enhanced ranking. WWW'10, pages 111-120. ACM, 2010.
3. Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A. and Vigna, S. The query-flow graph: model and applications. CIKM'08, pages 609-618. ACM, 2008.
4. Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E. and Li, H. Context-aware query suggestion by mining click-through and session data. In KDD'08, 2008.
5. Catledge, L.D. and Pitkow, J.E. Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN Systems, 27(6), 1995.
6. Craswell, N. and Szummer, M. Random walks on the click graph. SIGIR'07, pages 239-246, 2007.
7. Deng, H. and Irwin, K. and Michael L. Entropy-biased models for query representation on the click graph. SIGIR'09.
8. Donato, D., Bonchi, F., Chi, T. and Maarek, Y. Do you want to take notes? identifying research missions in yahoo! search pad. WWW'10, pages 321-330, New York, NY, USA, 2010. ACM.
9. Dunning, T. Accurate methods for the statistics of surprise and coincidence. Comput. Linguist., 19:61-74, March 1993.
10. Fox, S., Karnawat, K., Mydland, M., Dumais, S. and White, T. Evaluating implicit measures to improve web search. ACM Trans. Inf. Syst., 23:147-168, April 2005.
11. Gao, J., Yuan, W., Li, X., Deng, K. and Nie, J.-Y. Smoothing clickthrough data for web search ranking. SIGIR'09, pages 355-362. ACM, 2009.
12. Hassan, A., Jones, R., and Klinkner, K. Beyond dcg: user behavior as a predictor of a successful search. WSDM'10, pages 221-230, 2010.
13. Hassan, A., Song, Y. and He, L-w. A task level user satisfaction metric and its application on improving relevance estimation. CIKM'11, 2011.
14. Haveliwala T., Kamvar, S., and Jeh G. An analytical comparison of approaches to personalizing pagerank. 2003.
15. He, D., G̈oker, A. and Harper, D.J. Combining evidence for automatic web session identification. I.P.M., 38(5), 2002.
16. Huang, C.K., Chien, L.F. and Oyang, Y.J. Relevant term suggestion in interactive web search based on contextual information in query session logs. JASIST, 2003.
17. Jansen, B., Spink, A. and Kathuria, V. How to define searching sessions on web search engines. WebKDD'06, 2007.
18. Joachims, T. Optimizing search engines using clickthrough data. KDD'02. ACM, 2002.
19. Jones, R. and Klinkner, K.L. Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. CIKM'08. ACM, 2008.
20. Jones, R., Rey, B., Madani, O. and Greiner, W. Generating query substitutions. WWW'06, pages 387-396. ACM, 2006.
21. Kotov, A., Bennett, P., White, R., Dumais, S. and Teevan, J. Modeling and analysis of cross-session search tasks. SIGIR'11.
22. Liao, Z., Jiang, D., Chen, E., Pei, J., Cao, H. and Li, H. Mining concept sequences from large-scale search logs for context-aware query suggestion. ACM Trans. Intell. Syst. Technol., 3, 2011.
23. Liu, Y., Gao, B., Liu, T.-Y., Zhang, Y., Ma, Z., He, S. and Li, H. Browserank: letting web users vote for page importance. SIGIR'08, pages 451-458, 2008.
24. Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Identifying task-based sessions in search engine query logs. WSDM'11, pages 277-286. ACM, 2011.
25. MacQueen, J. Some methods for classification and analysis of multivariate observations. pages 281-297, 1967.
26. Martin E., Kriegel, H., Sander, J. and Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD'96, pages 226-231. AAAI Press, 1996.
27. Mei, Q. and Zhou, D. and Church, K. Query suggestion using hitting time. CIKM'08, pages 469-478. ACM, 2008.
28. Olston, C. and Chi, E.H. Scenttrails: Integrating browsing and searching on the web. ACM Trans. Comput.-Hum. Interact., 10:177-197, September 2003.
29. Shen, D., Pan, R., Sun, J.-T, Pan, J., Wu, K., Yin, J. and Yang, Q. Q2c@ust: our winning solution to query classification in kddcup 2005. SIGKDD Explor. Newsl., 7:100-110.
30. Shen, X., Tan, B. and Zhai, ChengXiang. Context-sensitive information retrieval using implicit feedback. SIGIR'05, pages 43-50, New York, NY, USA, 2005. ACM.
31. Silverstein, C., Henzinger, M.R., Marais, H. and Moricz, M. Analysis of a very large web search engine query log. SIGIR Forum, 33:6-12, 1999.
32. Swain, M. and Ballard, D. Color indexing. International Journal of Computer Vision, 7:11-32, 1991.
33. White, R. and Huang, J. Assessing the scenic route: measuring the value of search trails in web logs. SIGIR'10. ACM, 2010.
34. White, R., Bennett, P. and Dumais, S. Predicting short-term interests using activity-based search context. CIKM'10, 2010.
35. White, R., Bilenko, M. and Cucerzan, S. Studying the use of popular destinations to enhance web search interaction. SIGIR'07, pages 159-166. ACM, 2007.
36. Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E. and Li, H. Context-aware ranking in web search. SIGIR'10. ACM, 2010.