Using online relevance feedback to build effective personalized metasearch engine
Zhu Shanfeng, Xiaotie Deng, Kang Chen, Zheng Weimin · 2005
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.