Query reformulation mining: Models, patterns, and applications

Information Retrieval

Volumn 14, Issue 3, 2011, Pages 257-289

  Boldi, Paolo ^a   Bonchi, Francesco ^b   Castillo, Carlos ^b   Vigna, Sebastiano ^a  

^a UNIVERSITY OF MILAN   (Italy)

^b YAHOO RESEARCH   (Spain)

Author keywords

Query flow graph; Query log mining; Query recommendation; Session segmentation

Indexed keywords

EID: 79955646747     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-010-9155-3     Document Type: Article

References (44)

1. Anagnostopoulos, A., Becchetti, L., Castillo, C., & Gionis, A. (2010). An optimization framework for query recommendation. In WSDM '10: Proceedings of the third ACM international conference on web search and data mining (pp. 161-170). New York, NY, USA: ACM. doi: 10. 1145/1718487. 1718508.
2. Baeza-yates, R., Hurtado, C., & Mendoza, M. (2004). Query recommendation using query logs in search engines. In International workshop on clustering information over the web (ClustWeb, in conjunction with EDBT), Creete (pp. 588-596). New York: Springer.
3. Baeza-Yates, R., & Tiberi, A. (2007). Extracting semantic relations from query logs. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 76-85). New York, NY, USA: ACM Press. doi: 10. 1145/1281192. 1281204.
4. Baraglia, R., Castillo, C., Donato, D., Nardini, F. M., & Perego, R. (2010). The effects of time on query flow graph-based models for query suggestion. In Proceedings of RIAO.
5. Baraglia, R., Castillo, C., Donato, D., Nardini, F. M., Perego, R., & Silvestri, F. (2009). Aging effects on query flow graphs for query suggestion. In CIKM '09: Proceeding of the 18th ACM conference on information and knowledge management (pp. 1947-1950). New York, NY, USA: ACM. doi: 10. 1145/1645953. 1646272.
6. Beeferman, D., & Berger, A. (2000). Agglomerative clustering of a search engine query log. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 407-416). New York: ACM Press. doi: 10. 1145/347090. 347176.
7. Belazzougui, D., Boldi, P., Pagh, R., & Vigna, S. (2009). Theory and practise of monotone minimal perfect hashing. In Proceedings of the tenth workshop on algorithm engineering and experiments (ALENEX) (pp. 132-144). SIAM.
8. Belkin, N. J. (2000). The human element: Helping people find what they don't know. Communications of the ACM, 43(8), 58-61.
9. Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., & Vigna, S. (2008). The query-flow graph: Model and applications. In Proceedings of ACM 17th conference on information and knowledge management (CIKM) (pp. 609-618). Napa Valley, CA, USA: ACM Press.
10. Boldi, P., Bonchi, F., Castillo, C., Donato, D., & Vigna, S. (2009). Query suggestions using query-flow graphs. In WSCD '09: Proceedings of the 2009 workshop on web search click data (pp. 56-63). New York, NY, USA: ACM. doi: 10. 1145/1507509. 1507518.
11. Boldi, P., Bonchi, F., Castillo, C., & Vigna, S. (2009). From "dango" to "japanese cakes": Query reformulation models and patterns. In WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM international joint conference on web intelligence and intelligent agent technology (pp. 183-190). Washington, DC, USA: IEEE Computer Society. doi: 10. 1109/WI-IAT. 2009. 34.
12. Boldi, P., & Vigna, S. (2004). The WebGraph framework I: Compression techniques. In Proceedings of the thirteenth international world wide web conference (WWW 2004) (pp. 595-601). Manhattan, USA: ACM Press.
13. Bordino, I., Castillo, C., Donato, D., & Gionis, A. (2010). Query similarity by projecting the query-flow graph. In SIGIR '10: Proceedings of the 33rd annual international ACM SIGIR conference on research and development in information retrieval. New York: ACM Press.
14. Castillo, C., Donato, D., Becchetti, L., Boldi, P., Leonardi, S., Santini, M., & Vigna, S. (2006). A reference collection for web spam. SIGIR Forum, 40(2), 11-24. doi: 10. 1145/1189702. 1189703.
15. Craswell, N., & Szummer, M. (2007). Random walks on the click graph. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (pp. 239-246). New York, NY, USA: ACM Press. doi: 10. 1145/1277741. 1277784.
16. Craswell, N., Zoeter, O., Taylor, M., & Ramsey, B. (2008). An experimental comparison of click position-bias models. In WSDM '08: Proceedings of the international conference on web search and web data mining (pp. 87-94). New York: ACM.
17. Donato, D., Bonchi, F., Chi, T., & Maarek, Y. (2010). Do you want to take notes? Identifying research missions in Yahoo! Search Pad. In Proceedings of the 19th international conference on world wide web (WWW 2010).
18. Fonseca, B. M., Golgher, P. B., de Moura, E. S., & Ziviani, N. (2003). Using association rules to discover search engines related queries. In LA-WEB '03: Proceedings of the first latin American web congress. Washington, DC, USA: IEEE Computer Society.
19. Fuxman, A., Tsaparas, P., Achan, K., & Agrawal, R. (2008). Using the wisdom of the crowds for keyword generation. In WWW '08: Proceeding of the 17th international conference on world wide web (pp. 61-70). New York, NY, USA: ACM. doi: 10. 1145/1367497. 1367506.
20. Glance, N. S. (2001). Community search assistant. In Artificial intelligence for web search (pp. 91-96).
21. Goodrum, A. A., Bejune, M. M., & Siochi, A. C. (2003). A state transition analysis of image search patterns on the web. In Image and video retrieval, Lecture notes in computer science (Vol. 2728, pp. 193-197). New Yok: Springer.
22. Haas, S. W., & Grams, E. S. (1998). Page and link classifications: Connecting diverse resources. In DL '98: Proceedings of the third ACM conference on digital libraries (pp. 99-107). New York, NY, USA: ACM. doi: 10. 1145/276675. 276686.
23. He, D., & Göker, A. (2000). Detecting session boundaries from web user logs. In Proceedings of the BCS-IRSG 22nd annual colloquium on information retrieval research (pp. 57-66). Cambridge, UK.
24. He, D., Göker, A., & Harper, D. J. (2002). Combining evidence for automatic web session identification. Information Processing & Management 38(5), 727-742. doi: 10. 1016/S0306-4573(01)00060-7.
25. Jansen, B. J., Spink, A., Bateman, J., & Saracevic, T. (1998). Real life information retrieval: A study of user queries on the web. SIGIR Forum, 32(1), 5-17.
26. Jansen, B. J., Spink, A., & Narayan, B. (2007). Query modifications patterns during web searching. In Information technology, 2007. ITNG '07. fourth international conference on (pp. 439-444). doi: 10. 1109/ITNG. 2007. 164.
27. Jansen, B. J., Zhang, M., & Spink, A. (2007). Patterns and transitions of query reformulation during web searching. International Journal of Web Information Systems, 3(4), 328-340. doi: 10. 1108/17440080710848116.
28. Jeh, G., & Widom, J. (2003). Scaling personalized web search. In WWW '03: Proceedings of the 12th international conference on world wide web (pp. 271-279). New York, NY, USA: ACM Press. doi: 10. 1145/775152. 775191.
29. Jones, R., & Klinkner, K. L. (2008). 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 (pp. 699-708). New York, NY, USA: ACM. doi: 10. 1145/1458082. 1458176.
30. Jones, R., Rey, B., Madani, O., & Greiner, W. (2006). Generating query substitutions. In Proceedings of the 15th international conference on world wide web, WWW 2006 (pp. 387-396). Scotland, UK: Edinburgh.
31. Lau, T., & Horvitz, E. (1999). Patterns of search: Analyzing and modeling web query refinement. In UM '99: Proceedings of the seventh international conference on user modeling (pp. 119-128). New York: Springer.
32. Li, Y., Zheng, Z., & Dai, H. (2005). Kdd cup-2005 report: Facing a great challenge. SIGKDD Explorations Newsletter, 7(2), 91-99. doi: 10. 1145/1117454. 1117466.
33. Luxenburger, J., Elbassuoni, S., & Weikum, G. (2008). Matching task profiles and user needs in personalized web search. In CIKM '08: Proceeding of the 17th ACM conference on information and knowledge mining (pp. 689-698). New York, NY, USA: ACM. doi: 10. 1145/1458082. 1458175.
34. Mei, Q., Zhou, D., & Church, K. (2008). Query suggestion using hitting time. In CIKM '08: Proceeding of the 17th ACM conference on information and knowledge mining (pp. 469-478). New York, NY, USA: ACM. doi: 10. 1145/1458082. 1458145.
35. Quinlan, J. R. (1993). C4. 5: Programs for machine learning. MA: Morgan Kaufmann.
36. Radlinski, F., & Joachims, T. (2005). Query chains: Learning to rank from implicit feedback. In KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining (pp. 239-248). New York, NY, USA: ACM Press. doi: 10. 1145/1081870. 1081899.
37. Richardson, M. (2008). Learning about the world through long-term query logs. ACM Transactions on the Web, 2(4), 1-27. doi: 10. 1145/1409220. 1409224.
38. 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. doi: 10. 1016/j. ipm. 2005. 05. 005.
39. Sadikov, E., Madhavan, J., Wang, L., & Halevy, A. (2010). Clustering query refinements by user intent. In WWW '10: Proceedings of the 19th international conference on world wide web. North Carolina, USA: Raleigh.
40. Silverstein, C., Henzinger, M., Marais, H., & Moricz, M. (1998). Analysis of a very large altavista query log. Tech. rep., Digital SRC.
41. Spink, A., Jansen, B. J., Wolfram, D., & Saracevic, T. (2002). From e-sex to e-commerce: Web search changes. IEEE Computer, 35(3), 107-109.
42. Wen, J., Nie, J., & Zhang, H. (2001). Clustering user queries of a search engine. In Proceedings of the 10th international conference on world wide web (pp. 162-168). New York: ACM.
43. White, R. W., Clarke, C. L. A., & Cucerzan, S. (2007). Comparing query logs and pseudo-relevance feedbackfor web-search query refinement. In SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (pp. 831-832). New York, NY, USA: ACM. doi: 10. 1145/1277741. 1277931.
44. Zhang, Z., & Nasraoui, O. (2006). Mining search engine query logs for query recommendation. In WWW '06: Proceedings of the 15th international conference on world wide web (pp. 1039-1040). New York, NY, USA: ACM. doi: 10. 1145/1135777. 1136004.