By Topic

Using online relevance feedback to build effective personalized metasearch engine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Zhu Shanfeng ; Dept. of Comput. Sci., City Univ. of Hong Kong, China ; Deng Xiaotie ; Chen Kang ; Zheng Weimin

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.

Published in:

Web Information Systems Engineering, 2001. Proceedings of the Second International Conference on  (Volume:1 )

Date of Conference:

3-6 Dec. 2001