Behavior-driven clustering of queries into topics

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 1373-1382

  Aiello, Luca Maria ^a   Donato, Debora ^b   Ozertem, Umut ^b   Menczer, Filippo ^c  

^a UNIVERSITY OF TURIN   (Italy)

^b YAHOO RESEARCH   (United States)

^c INDIANA UNIVERSITY   (United States)

Author keywords

mission; query log; topic; user intent; user profiling

Indexed keywords

MISSION; QUERY LOGS; TOPIC; USER INTENT; USER PROFILING;

KNOWLEDGE MANAGEMENT; SEARCH ENGINES; WORLD WIDE WEB;

INFORMATION RETRIEVAL;

EID: 83055161599     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063775     Document Type: Conference Paper

References (37)

1. P. Anick. Using terminological feedback for web search refinement: a log-based study. In SIGIR '03, 2003.
2. R. Baeza-Yates. Graphs from search engine queries. In SOFSEM'07: 33rd conference on Current Trends in Theory and Practice of Computer Science, pages 1-8, Berlin, Heidelberg, 2007. Springer-Verlag.
3. R. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. In KDD'07: 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 76-85, New York, NY, USA, 2007. ACM.
4. N. Balasubramanian and S. Cucerzan. Automatic generation of topic pages using query-based aspect models. In CIKM'09: 18th ACM conference on Information and knowledge management, pages 2049-2052, New York, NY, USA, 2009. ACM.
5. A. L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207, 2005.
6. D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. In KDD'00: 6th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 407-416, New York, NY, USA, 2000. ACM.
7. S. M. Beitzel, E. C. Jensen, D. D. Lewis, A. Chowdhury, and O. Frieder. Automatic classification of web queries using very large unlabeled query logs. ACM Transactions on Information Systems, 25, April 2007.
8. P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. The query-flow graph: model and applications. In CIKM'08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 609-618, New York, NY, USA, 2008. ACM.
9. I. Bordino, C. Castillo, D. Donato, and A. Gionis. Query similarity by projecting the query-flow graph. In SIGIR'10, pages 515-522. ACM, 2010.
10. U. Brandes, D. Delling, M. Höfer, M. Gaertler, R. Görke, Z. Nikoloski, and D. Wagner. On Finding Graph Clusterings with Maximum Modularity. In WG'07: Proceedings of the 33rd International Workshop on Graph-Theoretic Concepts in Computer Science, Lecture Notes in Computer Science, 2007.
11. 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'08: 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 875-883, New York, NY, USA, 2008. ACM.
12. S.-L. Chuang and L.-F. Chien. A practical web-based approach to generating topic hierarchy for text segments. In CIKM'04: 13th ACM international conference on Information and knowledge management, pages 127-136, New York, NY, USA, 2004. ACM.
13. D. Donato, F. Bonchi, T. Chi, and Y. Maarek. Do you want to take notes?: identifying research missions in Yahoo! search pad. In WWW'10: 19th international conference on World wide web, pages 321-330, New York, NY, USA, 2010. ACM.
14. L. Fitzpatrick and M. Dent. Automatic feedback using past queries: social searching? In SIGIR '97, 1997.
15. S. Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75-174, 2010.
16. S. Fortunato and M. Barthèlemy. Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1):36-41, Jan 2007. (Pubitemid 46075032)
17. S. Gollapudi and R. Panigrahy. Exploiting asymmetry in hierarchical topic extraction. In CIKM'06: 15th ACM international conference on Information and knowledge management, pages 475-482, New York, NY, USA, 2006. ACM.
18. X. He and P. Jhala. Regularized query classification using search click information. Pattern Recognition, 41:2283-2288, July 2008.
19. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR'99, pages 50-57, 1999.
20. R. Jones and K. L. Klinkner. Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In CIKM'08: Proceeding of the 17th ACM conference on Information and knowledge management, pages 699-708, New York, NY, USA, 2008. ACM.
21. R. Jones and K. L. Klinkner. Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In CIKM '08: 17th ACM conference on Information and knowledge mining, pages 699-708, New York, NY, USA, 2008. ACM.
22. A. Lancichinetti, S. Fortunato, and F. Radicchi. Benchmark graphs for testing community detection algorithms. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 78(4), 2008.
23. A. Lancichinetti, F. Radicchi, J. J. Ramasco, and S. Fortunato. Finding statistically significant communities in networks. PLoS ONE, 6(4), 04 2011.
24. J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Mahoney. Statistical properties of community structure in large social and information networks. In WWW'08: Proceedings of the 17th international conference on World Wide Web, pages 695-704, New York, NY, USA, 2008. ACM.
25. M. Molloy and B. Reed. A critical point for random graphs with a given degree sequence. Random Structures & Algorithms, 6:161-179, March 1995.
26. M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69, 2004.
27. F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In KDD'05: 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pages 239-248, New York, NY, USA, 2005. ACM.
28. V. V. Raghavan and H. Sever. On the reuse of past optimal queries. In SIGIR '95, 1995.
29. G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
30. L. Sarmento, A. Kehlenbeck, E. Oliveira, and L. Ungar. Efficient clustering of web-derived data sets. In MLDM '09: 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 398-412, Berlin, Heidelberg, 2009. Springer-Verlag.
31. W. Song, Y. Zhang, T. Liu, and S. Li. Bridging topic modeling and personalized search. In COLING'10: 23rd International Conference on Computational Linguistics: Posters, pages 1167-1175, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
32. A. Veilumuthu and P. Ramachandran. Intent based clustering of search engine query log. In CASE'09: 5th IEEE international conference on Automation science and engineering, pages 647-652, Piscataway, NJ, USA, 2009. IEEE.
33. J.-R. Wen, J.-Y. Nie, and Z. Hong-Jiang. Query clustering using user logs. ACM Transactions on Information Systems, 20:59-81, January 2002. (Pubitemid 44642307)
34. I. H. Witten and E. Frank. Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, 1999.
35. J. Ye, J.-H. Chow, J. Chen, and Z. Zheng. Stochastic gradient boosted distributed decision trees. In CIKM'09: 18th ACM conference on Information and knowledge management, pages 2061-2064, New York, NY, USA, 2009. ACM.
36. J. Yi and F. Maghoul. Query clustering using click-through graph. In WWW'09: 18th international conference on World wide web, pages 1055-1056, New York, NY, USA, 2009. ACM.
37. H.-J. Zeng, Q.-C. He, Z. Chen, W.-Y. Ma, and J. Ma. Learning to cluster web search results. In SIGIR'04: 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 210-217, New York, NY, USA, 2004. ACM.