Fusion of effective retrieval strategies in the same information retrieval system

  • Authors:

  • Steven M. Beitzel;Eric C. Jensen;Abdur Chowdhury;David Grossman;Ophir Frieder;Nazli Goharian

  • Affiliations:

  • Information Retrieval Laboratory, Illinois Institute of Technology, Chicago, IL;Information Retrieval Laboratory, Illinois Institute of Technology, Chicago, IL;Information Retrieval Laboratory, Illinois Institute of Technology, Chicago, IL;Information Retrieval Laboratory, Illinois Institute of Technology, Chicago, IL;Information Retrieval Laboratory, Illinois Institute of Technology, Chicago, IL;Information Retrieval Laboratory, Illinois Institute of Technology, Chicago, IL

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2004

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Abstract

Prior efforts have shown that under certain situations retrieval effectiveness may be improved via the use of data fusion techniques. Although these improvements have been observed from the fusion of result sets from several distinct information retrieval systems, it has often been thought that fusing different document retrieval strategies in a single information retrieval system will lead to similar improvements. In this study, we show that this is not the case. We hold constant systemic differences such as parsing, stemming, phrase processing, and relevance feedback, and fuse result sets generated from highly effective retrieval strategies in the same information retrieval system. From this, we show that data fusion of highly effective retrieval strategies alone shows little or no improvement in retrieval effectiveness. Furthermore, we present a detailed analysis of the performance of modern data fusion approaches, and demonstrate the reasons why they do not perform well when applied to this problem. Detailed results and analyses are included to support our conclusions.