Web Search via Learning from Relevance Feedback

Web Search via Learning from Relevance Feedback

Xiannong Meng (Bucknell University, USA) and Zhixiang Chen (University of Texas-Pan Americana, USA)
DOI: 10.4018/978-1-59140-553-5.ch544
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Abstract

Recently, three general approaches have been taken to increase Web search accuracy and performance. One is the development of meta-search engines (e.g., MetaCrawler, www.metacrawler.com) that forward user queries to multiple search engines at the same time in order to increase the coverage and hope to include what the user wants in a short list of top-ranked results. Another approach is the development of topic-specific search engines that are specialized in particular topics. These topics range from vacation guides (www.vocations.com) to kids’ health (www.kidshealth.com). The third approach is to use some group or personal profiles to personalize the Web search. Examples of such efforts include GroupLens (Konstan et al., 1997).

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