Using online relevance feedback to build effective personalized metasearch engine

Author

Shanfeng, Zhu ; Xiaotie, Deng ; Kang, Chen ; Weimin, Zheng

Author_Institution

Dept. of Comput. Sci., City Univ. of Hong Kong, China

Volume

1

fYear

2001

fDate

3-6 Dec. 2001

Firstpage

262

Abstract

Metasearch Engine is popular for facilitating users´ queries over multiple search engines and increasing the coverage of the WWW. How to rank the merged results becomes crucial for the success of metasearch engines. Many current metasearch engines have poor precision, for one or more of selected source search engine returns irrelevant results. On the other hand, users with different interests may prefer distinct ranking order even for the same query. In this work, we try to use online relevance feedback to improve precision of the search results. At the same time, Users´ preferences are recorded during the process of feedback for future ranking. Our elementary experiment shows that it is effective in improving precision of the metasearch engine.

Keywords

relevance feedback; search engines; Metasearch Engine; online relevance feedback; search engine; search engines; search results; users´ queris; Computer science; Corporate acquisitions; Explosives; Feedback; Indexing; Metasearch; Publishing; Search engines; Web sites; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Information Systems Engineering, 2001. Proceedings of the Second International Conference on

Print_ISBN

0-7695-1393-X

Type

conf

DOI

10.1109/WISE.2001.996487

Filename

996487