Probability-based fusion of information retrieval result sets

Authors:
D. Lillis;F. Toolan;A. Mur;L. Peng;R. Collier;J. Dunnion
Affiliations:
School of Computer Science and Informatics, University College Dublin, Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Dublin, Ireland
Venue:
Artificial Intelligence Review
Year:
2006

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Abstract

Information Retrieval (IR) forms the basis of many information management tasks. Information management itself has become an extremely important area as the amount of electronically available information increases dramatically. There are numerous methods of performing the IR task both by utilising different techniques and through using different representations of the information available to us. It has been shown that some algorithms outperform others on certain tasks. Combining the results produced by different algorithms has resulted in superior retrieval performance and this has become an important research area. This paper introduces a probability-based fusion technique probFuse that shows initial promise in addressing this question. It also compares probFuse with the common CombMNZ data fusion technique.