Abstract
Rocchio’s relevance feedback method enhances the retrieval performance of the classical vector space model. However, its application to the probabilistic models is not adequately explored. In this paper, we revisit Rocchio’s algorithm by proposing to integrate this classical feedback method into the divergence from randomness (DFR) probabilistic framework for pseudo relevance feedback (PRF). Such an integration is denoted by RocDFR in this paper. In addition, we further improve RocDFR’s robustness by proposing a quality-biased feedback method, called QRocDFR. Extensive experiments on standard TREC test collections show that our proposed RocDFR and QRocDFR methods significantly outperform the relevance model (RM3), which is a representative feedback model in the language modeling framework. Moreover, the QRocDFR method considerably improves the robustness of RocDFR’s retrieval performance with respect to the size of feedback document set.
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Ye, Z., He, B., Huang, X., Lin, H. (2010). Revisiting Rocchio’s Relevance Feedback Algorithm for Probabilistic Models. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_14
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DOI: https://doi.org/10.1007/978-3-642-17187-1_14
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