Post-ranking query suggestion by diversifying search results

SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2011, Pages 815-824

  Song, Yang ^a   Zhou, Dengyong ^a   He, Li Wei ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

Learning to rank; Post ranking re ordering; Query suggestion; Random walk

Indexed keywords

RANDOM PROCESSES;

LABELED DATA; LEARNING TO RANK; QUERY SUGGESTION; RANDOM WALK; RANKING QUERIES; RE ORDERINGS;

SEARCH ENGINES;

EID: 80052128552     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2009916.2010025     Document Type: Conference Paper

References (25)

1. R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In Proceedings of WSDM '09, pages 5-14, New York, NY, USA, 2009. ACM.
2. R. Baeza-yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In In International Workshop on Clustering Information over the Web (ClustWeb, in conjunction with EDBT), Creete, pages 588-596. Springer, 2004. (Pubitemid 39750590)
3. R. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. In Proceedings of KDD '07, pages 76-85, New York, NY, USA, 2007. ACM. (Pubitemid 350229194)
4. P. N. Bennett and N. Nguyen. Refined experts: improving classification in large taxonomies. In SIGIR '09, pages 11-18, New York, NY, USA, 2009. ACM.
5. J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In Proceedings of SIGIR '98, pages 335-336, New York, NY, USA, 1998. ACM.
6. W. Chu and S. S. Keerthi. New approaches to support vector ordinal regression. In Proceedings of ICML '05, pages 145-152, New York, NY, USA, 2005. ACM. (Pubitemid 43183327)
7. K. Crammer, Y. Singer, N. Cristianini, J. Shawe-taylor, and B. Williamson. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:2001, 2001.
8. H. Deng, I. King, and M. R. Lyu. Entropy-biased models for query representation on the click graph. In SIGIR '09, pages 339-346, New York, NY, USA, 2009. ACM.
9. J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 2000. (Pubitemid 33405972)
10. K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst., 20:422-446, October 2002. (Pubitemid 44642296)
11. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In SIGIR '05, pages 154-161, New York, NY, USA, 2005. ACM.
12. M. G. Kendall. A New Measure of Rank Correlation. Biometrika, 30(1/2):81-93, June 1938.
13. P. Li, C. J. C. Burges, and Q. Wu. Mcrank: Learning to rank using multiple classification and gradient boosting. In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, editors, NIPS. MIT Press, 2007.
14. T.-Y. Liu. Learning to rank for information retrieval. Found. Trends Inf. Retr., 3:225-331, March 2009.
15. H. Ma, M. R. Lyu, and I. King. Diversifying query suggestion results. In AAAI, 2010.
16. H. Ma, H. Yang, I. King, and M. R. Lyu. Learning latent semantic relations from clickthrough data for query suggestion. In CIKM '08, pages 709-718, New York, NY, USA, 2008. ACM.
17. Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. In CIKM '08, pages 469-478, New York, NY, USA, 2008. ACM.
18. K. Pearson. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 302:157-175, 1900.
19. Y. Song and L. He. Optimal rare query suggestion with implicit user feedback. In WWW '10, pages 901-910, New York, NY, USA, 2010. ACM.
20. H. Tong, C. Faloutsos, and J.-Y. Pan. Random walk with restart: fast solutions and applications. Knowl. Inf. Syst., 14:327-346, March 2008.
21. I. Tsochantaridis. Support vector machine learning for interdependent and structured output spaces. PhD thesis, Providence, RI, USA, 2005. AAI3174684.
22. X. Wang, H. Fang, and C. Zhai. Improve retrieval accuracy for difficult queries using negative feedback. In CIKM '07, pages 991-994, New York, NY, USA, 2007. ACM.
23. Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Inf. Retr., 13:254-270, June 2010.
24. C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In CIKM '01, pages 403-410, New York, NY, USA, 2001. ACM. (Pubitemid 34974657)
25. C. Zhai and J. Lafferty. A risk minimization framework for information retrieval. Inf. Process. Manage., 42:31-55, January 2006. (Pubitemid 41130281)