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Intelligent Web Search through Adaptive Learning from Relevance Feedback

Intelligent Web Search through Adaptive Learning from Relevance Feedback
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Author(s): Zhixiang Chen (University of Texas at Pan American, USA), Binhai Zhu (Montana State University, USA) and Xiannong Meng (Bucknell University, USA)
Copyright: 2003
Pages: 15
Source title: Architectural Issues of Web-Enabled Electronic Business
Source Author(s)/Editor(s): V.K. Murthy (University of New South Wales at Australian Defence Force Academy, Australia) and Nansi Shi (University of South Australia, Australia)
DOI: 10.4018/978-1-59140-049-3.ch009

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Abstract

In this chapter, machine-learning approaches to real-time intelligent Web search are discussed. The goal is to build an intelligent Web search system that can find the user’s desired information with as little relevance feedback from the user as possible. The system can achieve a significant search precision increase with a small number of iterations of user relevance feedback. A new machine-learning algorithm is designed as the core of the intelligent search component. This algorithm is applied to three different search engines with different emphases. This chapter presents the algorithm, the architectures, and the performances of these search engines. Future research issues regarding real-time intelligent Web search are also discussed.

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