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Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1
Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine
Kyoto, Japan
December 03-December 06
ISBN: 0-7695-1393-X
ASCII Text
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Zhu Shanfeng, Deng Xiaotie, Chen Kang, Zheng Weimin, "Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine," Web Information Systems Engineering, International Conference on, vol. 1, pp. 0262, Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1, 2001.
 
 
BibTex
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@article{ 10.1109/WISE.2001.996487,
author = {Zhu Shanfeng and Deng Xiaotie and Chen Kang and Zheng Weimin},
title = {Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine},
journal ={Web Information Systems Engineering, International Conference on},
volume = {1},
year = {2001},
isbn = {0-7695-1393-X},
pages = {0262},
doi = {http://doi.ieeecomputersociety.org/10.1109/WISE.2001.996487},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Web Information Systems Engineering, International Conference on
TI - Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine
SN - 0-7695-1393-X
SP
EP
A1 - Zhu Shanfeng,
A1 - Deng Xiaotie,
A1 - Chen Kang,
A1 - Zheng Weimin,
PY - 2001
KW - Metasearch Engine
KW - Relevance Feedback
KW - Personalization
VL - 1
JA - Web Information Systems Engineering, International Conference on
ER -
 
Zhu Shanfeng, City University of Hong Kong
Deng Xiaotie, City University of Hong Kong
Chen Kang, Tsinghua University
Zheng Weimin, Tsinghua University
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
Index Terms:
Metasearch Engine,Relevance Feedback, Personalization
Citation:
Zhu Shanfeng, Deng Xiaotie, Chen Kang, Zheng Weimin, "Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine," wise, vol. 1, pp.0262, Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1, 2001
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