Actively predicting diverse search intent from user browsing behaviors

Proceedings of the 19th International Conference on World Wide Web, WWW '10

Volumn , Issue , 2010, Pages 221-230

  Cheng, Zhicong ^a   Gao, Bin ^b   Liu, Tie Yan ^b  

^a PEKING UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

contextual information; diversification; log mining; search intent; searchtrigger

Indexed keywords

BROWSING BEHAVIOR; CONTEXTUAL INFORMATION; INFORMATION NEED; LOG MINING; OPTIMIZATION ALGORITHMS; SEARCH PERFORMANCE; SEARCH QUERIES;

FORECASTING; INFORMATION RETRIEVAL; INFORMATION SCIENCE; SEARCH ENGINES;

WORLD WIDE WEB;

EID: 77954598392     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1772690.1772714     Document Type: Conference Paper

References (26)

1. R. A. Baeza-Yates, C. A. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In EDBT Workshops, pages 588-596, 2004.
2. R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999.
3. H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In KDD, pages 875-883, 2008.
4. C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, G. Hullender. Learning to Rank using Gradient Descent. In ICML, pages 89-96, 2005.
5. Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li. Learning to rank: From pairwise approach to listwise approach. In ICML, pages 129-136, 2007.
6. O. Chapelle, B. Scholkopf, and A. Zien. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
7. C. L.A. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan, S. Buttcher, and I. MacKinnon. Novelty and diversity in information retrieval evaluation. In SIGIR, pages 659-666, 2008.
8. D. Cossock and T. Zhang. Subset ranking using regression. In COLT, pages 605-619, 2006.
9. Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 2003 (4).
10. S. Gollapudi and A. Sharma. An Axiomatic Approach for Result Diversification. In WWW, pages 381-390, 2009.
11. M. P. Grineva, M. N. Grinev, and D. Lizorkin. Extracting key terms from noisy and multitheme documents. In WWW, pages 661-670, 2009.
12. T. Joachims. Optimizing search engines using clickthrough data. In KDD, pages 133-142, 2002.
13. R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In WWW, pages 387-396, 2006.
14. R. Mihalcea and A. Csomai. Wikify!: linking documents to encyclopedic knowledge. In CIKM, pages 233-242, 2007.
15. F. Radlinski, R. Kleinberg, and T. Joachims. Learning Diverse Rankings with Multi-Armed Bandits. In ICML, 2008.
16. J. Shi and J. Malik. Normalized cuts and image segmentation. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:888-905, 2000.
17. R. W. White, M. Bilenko, and S. Cucerzan. Studying the use of popular destinations to enhance web search interaction. In SIGIR, pages 159-166, 2007.
18. R. W. White, P. Bailey, and L. Chen. Predicting user interests from contextual information. In SIGIR, pages 363-370, 2009.
19. I. H. Witten, G. W. Paynter, E. Frank, C. Gutwin, and C. G. Nevill-Manning. Kea: Practical automatic keyphrase extraction. In ACM DL, pages 254-255, 1999.
20. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to Ad Hoc information retrieval. In SIGIR, pages 334-342, 2001.
21. http://en.wikipedia.org/wiki/Correlation
22. http://en.wikipedia.org/wiki/Kullback- Leibler-divergence
23. http://en.wikipedia.org/wiki/Jensen- Shannon-divergence
24. http://searchengineland.com/nielsen-netratings-august- 2007-search-share-puts-google-on-top-microsoft- holding-gains-12243
25. http://www.accuracast.com/search-daily-news/seo- 7471/us-search-engine- market-share-data-jan-2009/
26. http://www.comscore.com/Press-Events/ Comunicados-de-prensa/2007/node- 1285/Top-US- Search-Engines