Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval

Authors:
D. Frank Hsu;Isak Taksa
Affiliations:
Department of Computer and Information Science, Fordham University, New York, USA 10023;Department of Statistics and Computer Information Systems, Baruch College, New York, USA 10010
Venue:
Information Retrieval
Year:
2005

Citing 11
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Abstract

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.