A Comparative Study of Pseudo Relevance Feedback for Ad-hoc Retrieval
A Comparative Study of Pseudo Relevance Feedback for Ad-hoc Retrieval
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DOI：
10.1007/978-3-642-23318-0_30
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发表时间：
2011-09
期刊：
影响因子：
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通讯作者：
Kai Hui;Ben He;Tiejian Luo;Bin Wang-
中科院分区：
文献类型：
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作者：
Kai Hui;Ben He;Tiejian Luo;Bin Wang-
本文对不同流行的伪相关反馈（PRF）方法的相对有效性进行了初步调查。通过对标准 TREC 测试集进行大量实验，比较了相关性模型的检索性能以及从 Rocchio 算法推广的两种基于 KL 散度的随机性散度 (DFR) 反馈方法。结果表明，基于 KL 散度的 DFR 方法（记为 KL1）与经典 Rocchio 算法相结合，在本文研究的三种方法中具有最佳的检索效果。
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 asKL1), combined with the classical Rocchio’s algorithm, has the best retrieval effectiveness out of the three methods studied in this paper.