A Personalized Metasearch Engine Based on
Multi-Agent System
Meijia Wang, Qingshan Li, Yishuai Lin, Yingjian
Li, and Boyu Zhou
Software
Engineering Institute, Xidian University, China
Abstract: By
providing unified access to multiple underlying search engines, metasearch
engine is an intuitiveway to increase the coverage of the WWW. Great progress
has been made in this area, butthe previous studies ignore the perspectives of
users. This paper proposes a personalization mechanism for metasearch engine
based on multi-agent system to improve precision ratio. The proposed mechanism
obtains user interests from click-through data, schedules the appropriate underlying search engines according to the expertness model, and merges results based on user interest
distribution. Moreover, it also has the ability to provide personalized result
recommendation. Compared with the baseline results, experimental results show
that the proposed personalization
mechanism performs better on precision. The proposed metasearch engine is feasible for providing
useful search results more effectively.
Keywords: Metasearch engine, multi-agent system, personalized search.
Received October 29, 2016; accepted August 26, 2018
