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MARS: Multiplicative Adaptive Refinement Web Search

MARS: Multiplicative Adaptive Refinement Web Search

Xiannong Meng, Zhixiang Chen
Copyright: © 2005 |Pages: 20
ISBN13: 9781591404149|ISBN10: 1591404142|ISBN13 Softcover: 9781591404156|EISBN13: 9781591404163
DOI: 10.4018/978-1-59140-414-9.ch005
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MLA

Meng, Xiannong, and Zhixiang Chen. "MARS: Multiplicative Adaptive Refinement Web Search." Web Mining: Applications and Techniques, edited by Anthony Scime, IGI Global, 2005, pp. 99-118. https://doi.org/10.4018/978-1-59140-414-9.ch005

APA

Meng, X. & Chen, Z. (2005). MARS: Multiplicative Adaptive Refinement Web Search. In A. Scime (Ed.), Web Mining: Applications and Techniques (pp. 99-118). IGI Global. https://doi.org/10.4018/978-1-59140-414-9.ch005

Chicago

Meng, Xiannong, and Zhixiang Chen. "MARS: Multiplicative Adaptive Refinement Web Search." In Web Mining: Applications and Techniques, edited by Anthony Scime, 99-118. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-414-9.ch005

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Abstract

This chapter reports the project MARS (Multiplicative Adaptive Refinement Search), which applies a new multiplicative adaptive algorithm for user preference retrieval to Web searches. The new algorithm uses a multiplicative query expansion strategy to adaptively improve and reformulate the query vector to learn users’ information preference. The algorithm has provable better performance than the popular Rocchio’s similarity-based relevance feedback algorithm in learning a user preference that is determined by a linear classifier with a small number of non-zero coefficients over the real-valued vector space. A meta-search engine based on the aforementioned algorithm is built, and analysis of its search performance is presented.

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