Performance weights for the linear combination data fusion method in information retrieval

Authors:
Shengli Wu;Qili Zhou;Yaxin Bi;Xiaoqin Zeng
Affiliations:
School of Computing and Mathematics, University of Ulster, Northern Ireland, UK;School of Computing, Hangzhou Dianzi University, Hangzhou, China;School of Computing and Mathematics, University of Ulster, Northern Ireland, UK;Department of Computer Science, Hohai University, Nanjing, China
Venue:
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Year:
2008

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Abstract

In information retrieval, the linear combination method is a very flexible and effective data fusion method, since different weights can be assigned to different component systems. However, it remains an open question which weighting schema is good. Previously, a simple weighting schema was very often used: for a system, its weight is assigned as its average performance over a group of training queries. In this paper, we investigate the weighting issue by extensive experiments. We find that, a series of power functions of average performance, which can be implemented as efficiently as the simple weighting schema, is more effective than the simple weighting schema for data fusion.