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A GA-based query optimization method for web information retrieval
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DOI：10.1016/j.amc.2006.07.044.png)
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摘要
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By a different use of relevance feedback (the order in which the relevant documents are retrieved, the terms of the relevant documents, and the terms of the irrelevant documents) in the design of fitness function, and by introducing three different genetic operators, we have developed a new genetic algorithm-based query optimization method on relevance feedback for Web information retrieval. Based on three benchmark test collections Cranfield, Medline and CACM, experiments have been carried out to compare our method with three well-known query optimization methods on relevance feedback: the traditional Ide Dec-hi method, the Horng and Yeh's GA-based method and the Lopez-Pujalte et al.'s GA-based method. The experiments show that our method can achieve better results. (c) 2006 Elsevier Inc. All rights reserved.
Keyword:
genetic algorithm
relevance feedback
information retrieval
query optimization
fitness function
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IF:
3.4
论文数:
2.3W
被引数:
3.3W
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