ABSTRACT
We present a new algorithm for improving retrieval results by combining document ranking functions: Condorcet-fuse. Beginning with one of the two major classes of voting procedures from Social Choice Theory, the Condorcet procedure, we apply a graph-theoretic analysis that yields a sorting-based algorithm that is elegant, efficient, and effective. The algorithm performs very well on TREC data, often outperforming existing metasearch algorithms whether or not relevance scores and training data is available. Condorcet-fuse significantly outperforms Borda-fuse, the analogous representative from the other major class of voting algorithms.
- J. A. Aslam and M. Montague. Models for metasearch. In Croft et~al. {7}, pages 276--284. Google ScholarDigital Library
- B. T. Bartell. Optimizing Ranking Functions: A Connectionist Approach to Adaptive Information Retrieval. PhD thesis, University of California, San Diego, 1994. Google ScholarDigital Library
- B. T. Bartell, G. W. Cottrell, and R. K. Belew. Automatic combination of multiple ranked retrieval systems. In W. B. Croft and C. van Rijsbergen, editors, SIGIR'94, Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 173--181, Dublin, Ireland, July 1994. Springer-Verlag, London. Google ScholarDigital Library
- N. Belkin, P. Kantor, C. Cool, and R. Quatrain. Combining evidence for information retrieval. In Harman {15}, pages 35--43.Google Scholar
- N. Craswell, D. Hawking, and P. Thistlewaite. Merging results from isolated search engines. In Proceedings of the Tenth Australasian Database Conference, Aukland, New Zealand, Jan. 1999. Springer-Verlag.Google Scholar
- W. B. Croft. Combining approaches to information retrieval. In W. B. Croft, editor, Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, The Kluwer International Series on Information Retrieval, chapter~1. Kluwer Academic Publishers, 2000. Google ScholarDigital Library
- W. B. Croft, D. J. Harper, D. H. Kraft, and J. Zobel, editors. SIGIR'01, Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA, Sept. 2001. ACM Press, New York.Google Scholar
- J. C. de~Borda. Mémoire sur les élections au scrutin. In Histoire de l'Academie Royale des Sciences. Paris, 1781.Google Scholar
- M. de~Condorcet. Essai sur l'application de l'analyse à la probabilité des decisions rendues à la pluralité des voix, 1785.Google Scholar
- H. L. Fisher and D. R. Elchesen. Effectiveness of combining title words and index terms in machine retrieval searches. Nature, 238:109--110, July 1972.Google ScholarCross Ref
- E. Fox, P. Ingwersen, and R. Fidel, editors. SIGIR'95, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, July 1995. ACM Press, New York. Google ScholarCross Ref
- E. A. Fox, M. P. Koushik, J. Shaw, R. Modlin, and D. Rao. Combining evidence from multiple searches. In D. Harman, editor, The First Text REtrieval Conference (TREC-1), pages 319--328, Gaithersburg, MD, USA, Mar. 1993. U.S. Government Printing Office, Washington D.C.Google Scholar
- E. A. Fox and J. A. Shaw. Combination of multiple searches. In Harman {15}, pages 243--249.Google Scholar
- K. L. Fox, O. Frieder, M. Knepper, and E. Snowberg. SENTINEL: A multiple engine information retrieval and visualization system. Journal of the ASIS, 50(7), May 1999. Google ScholarDigital Library
- D. Harman, editor. The Second Text REtrieval Conference (TREC-2), Gaithersburg, MD, USA, Mar. 1994. U.S. Government Printing Office, Washington D.C.Google ScholarCross Ref
- D. A. Hull, J. O. Pedersen, and H. Schütze. Method combination for document filtering. In H.-P. Frei, D. Harman, P. Schäuble, and R. Wilkinson, editors, SIGIR'96, Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 279--287, Zurich, Switzerland, Aug. 1996. ACM Press, New York. Google ScholarDigital Library
- J. S. Kelly. Social Choice Theory: An Introduction. Springer-Verlag, 1988.Google Scholar
- J. H. Lee. Combining multiple evidence from different properties of weighting schemes. In Fox et~al. {11}, pages 180--188. Google ScholarDigital Library
- J. H. Lee. Analyses of multiple evidence combination. In N. J. Belkin, A. D. Narasimhalu, and P. Willett, editors, SIGIR'97, Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 267--275, Philadelphia, Pennsylvania, USA, July 1997. ACM Press, New York. Google ScholarDigital Library
- M. Montague and J. A. Aslam. Metasearch consistency. In Croft et~al. {7}, pages 386--387. Google ScholarDigital Library
- H. Moulin. Axioms of Cooperative Decision Making. Cambridge University Press, 1988.Google ScholarCross Ref
- K. B. Ng. An Investigation of the Conditions for Effective Data Fusion in Information Retrieval. PhD thesis, School of Communication, Information, and Library Studies, Rutgers University, 1998.Google Scholar
- K. B. Ng and P. B. Kantor. An investigation of the preconditions for effective data fusion in IR: A pilot study. In Proceedings of the 61th Annual Meeting of the American Society for Information Science, 1998.Google Scholar
- K. B. Ng, D. Loewenstern, C. Basu, H. Hirsh, and P. B. Kantor. Data fusion of machine learning methods for the TREC5 routing task (and other work). In Voorhees and Harman {35}, pages 477--487.Google Scholar
- W. H. Riker. Liberalism Against Populism. Waveland Press, 1982.Google Scholar
- E. W. Selberg. Towards Comprehensive Web Search. PhD thesis, University of Washington, 1999. Google ScholarDigital Library
- J. A. Shaw and E. A. Fox. Combination of multiple searches. In D. Harman, editor, Overview of the Third Text REtrieval Conference (TREC-3), pages 105--108, Gaithersburg, MD, USA, Apr. 1995. U.S. Government Printing Office, Washington D.C.Google Scholar
- B. Shu and S. Kak. A neural network-based intelligent metasearch engine. Information Sciences, 120:1--11, 1999. Google ScholarDigital Library
- P. Thompson. A combination of expert opinion approach to probabilistic information retrieval, part 1: the conceptual model. Information Processing and Management, 26(3):371--382, 1990. Google ScholarDigital Library
- P. Thompson. A combination of expert opinion approach to probabilistic information retrieval, part 2: mathematical treatment of CEO model 3. Information Processing and Management, 26(3):383--394, 1990. Google ScholarDigital Library
- C. C. Vogt. Adaptive Combination of Evidence for Information Retrieval. PhD thesis, University of California, San Diego, 1999. Google ScholarDigital Library
- C. C. Vogt. How much more is better? Characterizing the effects of adding more IR systems to a combination. In Content-Based Multimedia Information Access (RIAO), pages 457--475, Paris, France, Apr. 2000.Google Scholar
- C. C. Vogt and G. W. Cottrell. Fusion via a linear combination of scores. Information Retrieval, 1(3):151--173, Oct. 1999. Google ScholarDigital Library
- C. C. Vogt, G. W. Cottrell, R. K. Belew, and B. T. Bartell. Using relevance to train a linear mixture of experts. In Voorhees and Harman {35}, pages 503--515.Google Scholar
- E. Voorhees and D. Harman, editors. The Fifth Text REtrieval Conference (TREC-5), Gaithersburg, MD, USA, 1997. U.S. Government Printing Office, Washington D.C.Google ScholarCross Ref
- E. M. Voorhees, N. K. Gupta, and B. Johnson-Laird. Learning collection fusion strategies. In Fox et~al. {11}, pages 172--179. Google ScholarDigital Library
- Condorcet fusion for improved retrieval
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