Learning to rank query suggestions for adhoc and diversity search

Information Retrieval

Volumn 16, Issue 4, 2013, Pages 429-451

  Santos, Rodrygo L T ^a   Macdonald, Craig ^a   Ounis, Iadh ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Diversity; Learning to rank; Query suggestions; Relevance; Web search

Indexed keywords

EID: 84880836025     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-012-9211-2     Document Type: Article

References (50)

1. Alonso, O., Rose, D. E., & Stewart, B. (2008). Crowdsourcing for relevance evaluation. SIGIR Forum, 42(2), 9-15.
2. Amati, G. (2003). Probabilistic models for information retrieval based on divergence from randomness. PhD thesis.: University of Glasgow.
3. Amati, G., Ambrosi, E., Bianchi, M., Gaibisso, C., & Gambosi, G. (2007). FUB, IASI-CNR and University of Tor Vergata at TREC 2007 Blog track. In Proceedings of TREC.
4. Baeza-Yates, R. A., Hurtado, C. A., & Mendoza, M. (2004). Query recommendation using query logs in search engines. In Proceedings of ClustWeb at EDBT (pp. 588-596).
5. Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., & Vigna, S. (2008). The query-flow graph: Model and applications. In Proceedings of CIKM (pp. 609-618).
6. Boldi, P., Bonchi, F., Castillo, C., Donato, D., & Vigna, S. (2009). Query suggestions using query-flow graphs. In Proceedings of WSCD at WSDM (pp. 56-63).
7. Broccolo, D., Marcon, L., Nardini, F. M., Perego, R., & Silvestri, F. (2012). Generating suggestions for queries in the long tail with an inverted index. Information Processing and Management, 48(2), 326-339.
8. Burges, C. J. C. (2010). From RankNet to LambdaRank to LambdaMART: An overview. Technical report MSR-TR-2010-82, Microsoft Research.
9. Carterette, B., Allan, J., & Sitaraman, R. (2006). Minimal test collections for retrieval evaluation. In Proceedings of SIGIR (pp. 268-275).
10. Carterette, B., Pavlu, V., Kanoulas, E., Aslam, J. A., & Allan, J. (2009). If I dad a million queries. In Proceedings of ECIR (pp. 288-300). New York: Springer.
11. Chapelle, O., & Chang, Y. (2011). Yahoo! learning to rank challenge overview. Journal of Machine Learning Research, 14, 1-24.
12. Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009). Expected reciprocal rank for graded relevance. In Proceedings of CIKM (pp. 621-630).
13. Clarke, C. L. A., Craswell, N., & Soboroff, I. (2009). Overview of the TREC 2009 Web track. In Proceeding of TREC.
14. Clarke, C. L. A., Craswell, N., Soboroff, I., & Ashkan, A. (2011). A comparative analysis of cascade measures for novelty and diversity. In Proceedings of WSDM (pp. 75-84).
15. Clarke, C. L. A., Craswell, N., Soboroff, I., & Cormack, G. V. (2010). Overview of the TREC 2010 Web track. In Proceedings of TREC.
16. Clarke, C. L. A., Craswell, N., Soboroff, I., & Voorhees, E. M. (2011). Overview of the TREC 2011 Web track. In Proceedidngs of TREC.
17. Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., & MacKinnon, I. (2008). Novelty and diversity in information retrieval evaluation. In Proceedings of SIGIR (pp. 659-666).
18. Clarke, C. L. A., Kolla, M., & Vechtomova, O. (2009). An effectiveness measure for ambiguous and underspecified queries. In Proceedings of ICTIR (pp. 188-199).
19. Cucerzan, S., & White, R. W. (2007). Query suggestion based on user landing pages. In Proceedings of SIGIR (pp. 875-876). New York: ACM.
20. Dang, V., Bendersky, M., & Croft, W. B. (2010). Learning to rank query reformulations. In Proceedings of SIGIR (pp. 807-808).: ACM.
21. Dean, J. (2009). Challenges in building large-scale information retrieval systems: invited talk. In Proceedings of WSDM (p. 1). New York: ACM.
22. Downey, D., Dumais, S., & Horvitz, E. (2007). Heads and tails: studies of web search with common and rare queries. In Proceedings of SIGIR (pp. 847-848).
23. Fonseca, B. M., Golgher, P. B., De Moura, E. S., Pôssas, B., & Ziviani, N. (2003). Discovering search engine related queries using association rules. Journal of Web Engineering, 2, 215-227.
24. Ganjisaffar, Y., Caruana, R., & Lopes, C. (2011). Bagging gradient-boosted trees for high precision, low variance ranking models. In Proceedings of SIGIR (pp. 85-94), Beijing, China.
25. Hauff, C., Kelly, D., & Azzopardi, L. (2010). A comparison of user and system query performance predictions. In Proceedings of CIKM (pp. 979-988).
26. 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.
27. Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), 422-446.
28. Jones, R., Rey, B., Madani, O., & Greiner, W. (2006). Generating query substitutions. In Proceedings of WWW (pp. 387-396).
29. Liu, T.-Y. (2009). Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3), 225-331.
30. Mei, Q., Zhou, D., & Church, K. (2008). Query suggestion using hitting time. In Proceedings of CIKM (pp. 469-478).
31. Metzler, D. (2007). Automatic feature selection in the Markov random field model for information retrieval. In Proceedings of CIKM (pp. 253-262).
32. Metzler, D., & Croft, W. B. (2005). A Markov random field model for term dependencies. In Proceedings of SIGIR (pp. 472-479).
33. Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., & Lioma, C. (2006). Terrier: A high performance and scalable information retrieval platform. In Proceedings of OSIR at SIGIR.
34. Peng, J., Macdonald, C., He, V., Plachouras, V., & Ounis, I. (2007). Incorporating term dependency in the DFR framework. In Proceedings of SIGIR. New York: ACM Press.
35. Qin, T., Liu, T.-Y., Xu, J., & Li, H. (2009). LETOR: A benchmark collection for research on learning to rank for information retrieval. Information Retrieval, 13(4), 347-374.
36. Robertson, S. (2008). On the optimisation of evaluation metrics. In Proceedings of LR4IR at SIGIR.
37. Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M., & Gatford, M. (1994). Okapi at TREC-3. In Proceedings of TREC.
38. Santos, R. L. T., Macdonald, C., & Ounis, I. (2010). Exploiting query reformulations for web search result diversification. In Proceedings of WWW (pp. 881-890).
39. Santos, R. L. T., Macdonald, C., & Ounis, I. (2011). How diverse are web search results? In Proceedings of SIGIR (pp. 1187-1188).
40. Santos, R. L. T., Macdonald, C., & Ounis, I. (2011). Intent-aware search result diversification. In Proceedings of SIGIR (pp. 595-604).
41. Sheldon, D., Shokouhi, M., Szummer, M., & Craswell, N. (2011). LambdaMerge: merging the results of query reformulations. In Proceedings of WSDM (pp. 795-804).
42. Silvestri, F. (2010). Mining query logs: turning search usage data into knowledge. Foundations and Trends® in Information Retrieval, 4(1-2), 1-174.
43. Song, R., Luo, Z., Nie, J.-Y., Yu, Y., & Hon, H.-W. (2009). Identification of ambiguous queries in web search. Information Processing and Management, 45(2), 216-229.
44. Song, Y., Zhou, D., & Wei He, L. (2011). Post-ranking query suggestion by diversifying search results. In Proceedings of SIGIR (pp. 815-824). Beijing, China.
45. Spärck-Jones, K., Robertson, S. E., & Sanderson, M. (2007). Ambiguous requests: Implications for retrieval tests, systems and theories. SIGIR Forum, 41(2), 8-17.
46. Szpektor, I., Gionis, A., & Maarek, Y. (2011). Improving recommendation for long-tail queries via templates. In Proceedings of WWW (pp. 47-56).
47. Wang, X., & Zhai, C. (2008). Mining term association patterns from search logs for effective query reformulation. In Proceedings of CIKM (pp. 479-488).
48. Zaragoza, H., Craswell, N., Taylor, M. J., Saria, S., & Robertson, S. E. (2004). Microsoft Cambridge at TREC 13: Web and hard tracks. In Proceedings of TREC.
49. Zhai, C., & Lafferty, J. (2001). A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of SIGIR (pp. 334-342).
50. Zhang, Z., & Nasraoui, O. (2006). Mining search engine query logs for query recommendation. In Proceedings of WWW (pp. 1039-1040).