A comparative study of pseudo relevance feedback for ad-hoc retrieval

Authors:
Kai Hui;Ben He;Tiejian Luo;Bin Wang
Affiliations:
Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Institute of Computational Technology, Beijing, China
Venue:
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Year:
2011

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Abstract

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.