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Relevance Feedback for Text Retrieval

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  1. Carpineto C., de Mori R., Romano G., and Bigi B. An information-theoretic approach to automatic query expansion. ACM Trans. Inf. Syst., 19(1):1–27, 2001.

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  2. Spärck Jones K., Walker S., and Robertson S.E. A probabilistic model of information retrieval: development and comparative experiments. Inf. Process. Manage., 36(6):779–808 2000.(Part 1); 809–840 (Part 2),

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  3. Spink A., Jansen B.J., and Ozmultu H.C. Use of query reformulation and relevance feedback by Excite users. Internet Res.: Electron. Networking Appl. and Policy, 10(4):317–328, 2000.

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  4. White R.W., Ruthven I., and Jose J.M. A study of factors affecting the utility of implicit relevance feedback. In Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2005, pp. 35–42.

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Authors and Affiliations

    Authors
    1. Olga Vechtomova
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    Editor information

    Editors and Affiliations

    1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

      LING LIU (Professor) (Professor)

    2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

      M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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    © 2009 Springer Science+Business Media, LLC

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    Vechtomova, O. (2009). Relevance Feedback for Text Retrieval. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_949

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