DOI QR코드

DOI QR Code

A Study on Fuzzy Ranking Model based on User Preference

  • Kim Dae-Won (School of Computer Science and Engineering, Chung-Ang University)
  • Published : 2006.06.01

Abstract

A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. In this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

Keywords

References

  1. MJ. Martin-Baustista, D.H.Kraft, M.A.Vila, J. Chen, J.Cruz, User profils and fuzzy logic for web retrieval issues, Soft Computing, Vol. 6, 2002, 365-372 https://doi.org/10.1007/s00500-002-0190-x
  2. R.R.Yager and F.E.Petry, A framework for linguistic relevance feedback in content-based image retrieval using fuzzy logic, Information Sciences, In Press, April 2005
  3. A.F. Smeaton, Relevance feedback and a fuzzy set of search terms in an information retrieval system, Information Technology Research Development Applications archive, Vol. 3, Issue 1, 1984
  4. L.J.Kohout, Keravanou, E., and Bandler, W. Information retrieval system using fuzzy relational products for thesaurus construction. Proceedings IFAC Fuzzy Information, Marseille, France, 7-13,1983
  5. J.H. Lee, On the evaluation of Boolean operators in the extended boolean retrieval framework, Proceedings ofthe 17th SIGIR conference, 1994, 182-190
  6. R. Baeza-Yates, et al., Modern information retrieval, Addison-Wesley, 1999
  7. W.J. Wang, New similarity measures on fuzzy sets and on elements, Fuzzy Sets and Systems, Vol.85, 1997, 305-309 https://doi.org/10.1016/0165-0114(95)00365-7
  8. J. Fan, W. Xie, Some notes on similarity measure and proximity measure, Fuzzy Sets and Systems, Vol.101, 1999,403-412 https://doi.org/10.1016/S0165-0114(97)00108-5
  9. N. Stokes and J. Carthy, Combining semantic and syntactic document classifiers to improve first story detection, In proceedings of the 24th ACM SIGIR conference, New Orleans, 2001, pp. 424
  10. W. Gale, K. Church, and D. Yarowsky,Estimating upper and lower bounds on the performance of wordsense disambiguation programs, ACL, 1992
  11. S. Deerwester, S.T. Dumais, G.W. Furnas, TK. Landauer, and R. Harshman, Indexing by latent semantic analysis. Journal of the American Society of Information Science, vol. 41(6),1990. pp.391-407 https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
  12. TG. Kolda and D.P. O'Leary, A semidiscrete matrix decomposition for latent semantic indexing in information retrieval. In Proceedings of ACM Transaction of Information Systems, Vol. 16, 1998, pp. 322-346 https://doi.org/10.1145/291128.291131
  13. T A. Letsche and M. W. Berry, Large-scale information retrieval with latent semantic indexing, Information Sciences, Vol. 100, Issues 1-4, 1997, pp. 105-137 https://doi.org/10.1016/S0020-0255(96)00268-X
  14. C. Fellbaum et al., WordNet:An eletroic lexical database, The MIT press, 1998, pp.338-339