Comparing rank and score combination methods for data fusion in information retrieval

被引：112

作者：

Hsu D.F. ^{[1
,3
]}

Taksa I. ^{[2
]}

机构：

[1] Dept. of Comp. and Info. Science, Fordham University, New York, NY 10023, 113 West 60th Street

[2] Dept. of Stat. and Comp. Info. Syst., Baruch College, New York, NY 10010, One Bernard Baruch Way

[3] DIMACS Center, Rutgers University, Piscataway, NJ 08854-8018

来源：

Information Retrieval | 2005年 / 8卷 / 3期

基金：

美国国家科学基金会;

关键词：

Cayley graphs and digraphs; Data fusion (DF); Evidence combinations; Information retrieval (IR); Multiple evidences; Permutation; Rank combination; Rank/score function; Score combination; Symmetric group;

D O I：

10.1007/s10791-005-6994-4

中图分类号：

学科分类号：

摘要：

Combination of multiple evidences (multiple query formulations, multiple retrieval schemes or systems) has been shown (mostly experimentally) to be effective in data fusion in information retrieval. However, the question of why and how combination should be done still remains largely unanswered. In this paper, we provide a model for simulation and a framework for analysis in the study of data fusion in the information retrieval domain. A rank/score function is defined and the concept of a Cayley graph is used in the design and analysis of our framework. The model and framework have led us to better understanding of the data fusion phenomena in information retrieval. In particular, by exploiting the graphical properties of the rank/score function, we have shown analytically and by simulation that combination using rank performs better than combination using score under certain conditions. Moreover, we demonstrated that the rank/score function might be used as a predictive variable for the effectiveness of combination of multiple evidences. © 2005 Springer Science + Business Media, Inc.

引用

页码：449 / 480

页数：31

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