SOCIAL CHOICE FOR DATA FUSION

Shanfeng Zhu (), Qizhi Fang and Weimin Zheng
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Shanfeng Zhu: Proteome Informatics Donation Laboratory (SGJ Japan), Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
Qizhi Fang: Department of Applied Mathematics, Ocean University of China, Qingdao 266071, Shandong, P. R. China
Weimin Zheng: Department of Computer Science and Technology, Tsinghua University, Peking, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2004, vol. 03, issue 04, 619-631

Abstract: Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.

Keywords: Social choice; data fusion; graph theory; voting; Metasearch (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1142/S0219622004001288

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