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CIKM
2005
Springer

Using RankBoost to compare retrieval systems

10 years 3 months ago
Using RankBoost to compare retrieval systems
This paper presents a new pooling method for constructing the assessment sets used in the evaluation of retrieval systems. Our proposal is based on RankBoost, a machine learning voting algorithm. It leads to smaller pools than classical pooling and thus reduces the manual assessment workload for building test collections. Experimental results obtained on an XML document collection demonstrate the effectiveness of the approach according to different evaluation criteria. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Retrieval Models; I.2.6 [Artificial Intelligence]: Learning—parameter learning General Terms Algorithms, Experimentation, Measurement Keywords Pooling, RankBoost, XML retrieval evaluation
Huyen-Trang Vu, Patrick Gallinari
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2005
Where CIKM
Authors Huyen-Trang Vu, Patrick Gallinari
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