MARS: Multiplicative Adaptive Refinement Web Search

MARS: Multiplicative Adaptive Refinement Web Search

Xiannong Meng (Bucknell University, USA) and Zhixiang Chen (The University of Texas-Pan American, USA)
Copyright: © 2005 |Pages: 20
DOI: 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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