This paper presents an initial investigation in the relative effectiveness of different popular pseudo relevance feedback (PRF) methods. The retrieval performance of relevance model, and two KL-divergence-based divergence from randomness (DFR) feedback methods generalized from Rocchio's algorithm, are compared by extensive experiments on standard TREC test collections. Results show that a KL-divergence based DFR method (denoted as KL1), combined with the classical Rocchio's algorithm, has the best retrieval effectiveness out of the three methods studied in this paper. © 2011 Springer-Verlag.
CITATION STYLE
Hui, K., He, B., Luo, T., & Wang, B. (2011). A comparative study of pseudo relevance feedback for ad-hoc retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6931 LNCS, pp. 318–322). https://doi.org/10.1007/978-3-642-23318-0_30
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