Risky business: Modeling and exploiting uncertainty in information retrieval

Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009

Volumn , Issue , 2009, Pages 99-106

  Zhu, Jianhan ^a   Wang, Jun ^a   Cox, Ingemar J ^a   Taylor, Michael J ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

Language models; Loss functions; Probabilistic retrieval models; Probability ranking principle; Risk adjustment

Indexed keywords

LANGUAGE MODEL; LOSS FUNCTIONS; PROBABILISTIC RETRIEVAL; PROBABILISTIC RETRIEVAL MODELS; RISK ADJUSTMENT;

COMPUTATIONAL LINGUISTICS; INFORMATION RETRIEVAL; INFORMATION SERVICES; NATURAL LANGUAGE PROCESSING SYSTEMS; QUERY LANGUAGES; UNCERTAINTY ANALYSIS;

RISK PERCEPTION;

EID: 72449124739     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1571941.1571961     Document Type: Conference Paper

References (28)

1. G. Amati and C. J. V. Rijsbergen. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst., 20(4):357-389, 2002.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. In Proc. of NIPS, pages 601-608, 2001.
3. H. Chen and D. R. Karger. Less is more: probabilistic models for retrieving fewer relevant documents. In Proc. of SIGIR '06, pages 429-436, New York, NY, USA, 2006. ACM.
4. K. Church and W. Gale. Poisson mixtures. Journal of Natural Language Engineering, 1995.
5. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman and Hall, 2003.
6. D. Hiemstra. Using language models for information retrieval. Doctoral thesis, University of Twente, 2001.
7. T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Info. Syst., Vol 22(1):89-115, 2004.
8. F. Jelinek and R. Mercer. Interpolated estimation of markov source parameters from sparse data. Pattern Recognition in Practice, pages 381-402, 1980.
9. M. Kendall and A. Stuart, editors. The Advanced Theory of Statistics Volume 1, 3rd Edition (Section 3.12). Griffin, London, 1969.
10. J. D. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In Proc. of SIGIR '01, pages 111-119, 2001.
11. E. Lukacs, editor. Characteristic Functions, 2nd Edition (Page 27). Griffin, London, 1970.
12. R. E. Madsen, D. Kauchak, and C. Elkan. Modeling word burstiness using the Dirichlet distribution. In Proc. of ICML, pages 545-552, 2005.
13. M. E. Maron and J. L. Kuhns. On relevance, probabilistic indexing and information retrieval. J. ACM, 7(3):216-244, 1960.
14. J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proc. of SIGIR '98, pages 275-281, 1998.
15. J. Risson and T. Moors. Survey of research towards robust peer-to-peer networks: Search methods. Computer Networks, 50(17):3485-3521, 2006.
16. S. E. Robertson. The probability ranking principle in IR. Readings in information retrieval, pages 281-286, 1997.
17. S. E. Robertson, M. E. Maron, and W. Cooper. Probability of relevance: a unification of two competing models for document retrieval. Information Technology: Research and Development, 1(1):1-21, 1982.
18. S. E. Robertson and K. Sparck Jones. Relevance weighting of search terms. Journal of the American Society for Information Science, 27(3):129-146, 1976.
19. S. E. Robertson and S. Walker. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In Proc. of SIGIR '94, pages 232-241, 1994.
20. S. E. Robertson, S. Walker, M. Hancock-Beaulieu, M. Gatford, and A. Payne. Okapi at trec-4. In Text REtrieval Conference (TREC), 1995.
21. J. A. Thom and F. Scholer. A comparison of evaluation measures given how users perform on search tasks. In Australasian Document Computing Symposium, pages 100-103, 2007.
22. C. J. van Rijsbergen. Information Retrieval. Butterworths, London, London, UK, 1979.
23. J. Wang and J. Zhu. Portfolio theory of information retrieval. In ACM SIGIR 2009, 2009.
24. H. Zaragoza, D. Hiemstra, M. Tipping, and S. E. Robertson. Bayesian extension to the language model for ad hoc information retrieval. In Proc. of SIGIR '03, 2003.
25. A. Zellner. Bayesian estimation and prediction using asymmetric loss functions. Journal of the American Statistical Association, 81(394):446-451, 1986.
26. C. Zhai and J. D. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proc. of SIGIR '01, pages 334-342, 2001.
27. C. Zhai and J. D. Lafferty. A risk minimization framework for information retrieval. Inf. Process. Manage., 42(1):31-55, 2006.
28. J. Zhu, J. Wang, I. Cox, and M. Taylor. Risk-aware information retrieval. In Proc. of the European Conference on Information Retrieval (ECIR), pages 17-28, 2009.