Abstract
Recent developments in the field of data fusion have seen a focus on techniques that use training queries to estimate the probability that various documents are relevant to a given query and use that information to assign scores to those documents on which they are subsequently ranked. This paper introduces SlideFuse, which builds on these techniques, introducing a sliding window in order to compensate for situations where little relevance information is available to aid in the estimation of probabilities.
SlideFuse is shown to perform favourably in comparison with CombMNZ, ProbFuse and SegFuse. CombMNZ is the standard baseline technique against which data fusion algorithms are compared whereas ProbFuse and SegFuse represent the state-of-the-art for probabilistic data fusion methods.
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References
Bartell, B.T., Cottrell, G.W., Belew, R.K.: Automatic combination of multiple ranked retrieval systems. In: SIGIR 1994: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 173–181. Springer, New York (1994) Reference to show that it has long been demonstrated that fusion improves results
Beitzel, S.M., Jensen, E.C., Chowdhury, A., Grossman, D., Frieder, O., Goharian, N.: Fusion of effective retrieval strategies in the same information retrieval system. J. Am. Soc. Inf. Sci. Technol. 55(10), 859–868 (2004)
Vogt, C.C., Cottrell, G.W.: Fusion via a linear combination of scores. Information Retrieval 1(3), 151–173 (1999)
Aslam, J.A., Montague, M.: Bayes optimal metasearch: a probabilistic model for combining the results of multiple retrieval systems. In: SIGIR 2000: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 379–381. ACM Press, New York (2000)
Voorhees, E.M., Gupta, N.K., Johnson-Laird, B.: The collection fusion problem. In: Proceedings of the Third Text REtrieval Conference (TREC-3), pp. 95–104 (1994)
Lillis, D., Toolan, F., Collier, R., Dunnion, J.: ProbFuse: a probabilistic approach to data fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 139–146. ACM Press, New York (2006)
Shokouhi, M.: Segmentation of search engine results for effective data-fusion. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECiR 2007. LNCS, vol. 4425, Springer, Heidelberg (2007)
Fox, E.A., Shaw, J.A.: Combination of multiple searches. In: Proceedings of the 2nd Text REtrieval Conference (TREC-2), National Institute of Standards and Technology Special Publication 500-215, pp. 243–252 (1994)
Callan, J.P., Lu, Z., Croft, W.B.: Searching distributed collections with inference networks. In: SIGIR 1995: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 21–28. ACM Press, New York (1995)
Si, L., Callan, J.: Using sampled data and regression to merge search engine results. In: SIGIR 2002: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 19–26. ACM Press, New York (2002)
Montague, M., Aslam, J.A.: Condorcet fusion for improved retrieval. In: CIKM 2002: Proceedings of the eleventh international conference on Information and knowledge management, pp. 538–548. ACM Press, New York (2002)
Lee, J.H.: Analyses of multiple evidence combination. SIGIR Forum 31(SI), 267–276 (1997)
Voorhees, E.M., Gupta, N.K., Johnson-Laird, B.: Learning collection fusion strategies. In: SIGIR 1995: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 172–179. ACM Press, New York (1995)
Aslam, J.A., Montague, M.: Models for metasearch. In: SIGIR 2001: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 276–284. ACM Press, New York (2001)
Craswell, N., Hawking, D., Thistlewaite, P.B.: Merging results from isolated search engines. In: Australasian Database Conference, Auckland, New Zealand, pp. 189–200 (1999)
Lawrence, S., Giles, C.L.: Inquirus, the NECI meta search engine. In: Seventh International World Wide Web Conference, Brisbane, Australia, pp. 95–105. Elsevier, Amsterdam (1998)
Gravano, L., Chang, K., Garcia-Molina, H., Paepcke, A.: Starts: Stanford protocol proposal for internet retrieval and search. Technical report, Stanford, CA, USA (1997)
Lillis, D., Toolan, F., Collier, R., Dunnion, J.: Probabilistic data fusion on a large document collection. In: Proceedings of the 17th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2006), Belfast, Northern Ireland, Queen’s University Belfast (2006)
Craswell, N., Hawking, D.: Overview of the TREC-2004 web track. In: Proceedings of the Thirteenth Text REtrieval Conference (TREC-2004) (2004)
Buckley, C., Voorhees, E.M.: Retrieval evaluation with incomplete information. In: SIGIR 2004: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 25–32. ACM Press, New York (2004)
Silverstein, C., Henzinger, M., Marais, H., Moricz, M.: Analysis of a Very Large AltaVista Query Log. Technical Report 1998-014, Digital SRC (1998), http://gatekeeper.dec.com/pub/DEC/SRC/technical-notes/abstracts/src-tn-1998-014.html
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Lillis, D., Toolan, F., Collier, R., Dunnion, J. (2008). Extending Probabilistic Data Fusion Using Sliding Windows. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_33
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DOI: https://doi.org/10.1007/978-3-540-78646-7_33
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