Multiplicative Adaptive Refinement Web Search
نویسندگان
چکیده
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. INTRODUCTION Vector space models and relevance feedback have long been used in information retrieval (Baeza-Yates & Ribeiro-Neto, 1999; Salton, 1989). In the n-dimensional vector space model, a collection of n index terms or keywords is chosen, and any document d is represented by an n-dimensional vector d = (d 1 , ..., d n ), where d i represents the This chapter appears in the book, Web Mining: Applications a d Techniques, edited by Anthony Scime. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING
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Mars: Multiplicative Adaptive Refinement Web Search
This chapter reports the project MARS (Multiplicative Adaptive Refinement Search), which applies a new multiplicative adaptive algorithm for user preference retrieval to Web search. 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 ...
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