Bayes optimal metasearch: a probabilistic model for combining the results of multiple retrieval systems (poster session)

ABSTRACT

We introduce a new, probabilistic model for combining the outputs of an arbitrary number of query retrieval systems. By gathering simple statistics on the average performance of a given set of query retrieval systems, we construct a Bayes optimal mechanism for combining the outputs of these systems. Our construction yields a metasearch strategy whose empirical performance nearly always exceeds the performance of any of the constituent systems. Our construction is also robust in the sense that if “good” and “bad” systems are combined, the Performance of the composite is still on par with, or exceeds, that of the best constituent system. Finally, our model and theory provide theoretical and empirical avenues for the improvement of this metasearch strategy.

REFERENCES

• 1: E. A. Fox and J. A. Shaw. Combination of multiple searches. In The Second Text REtrieval Conference (TREC-P), pages 243-249, 1994.
• 2: Joon Ho Lee, Analyses of multiple evidence combination, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, p.267-276, July 27-31, 1997, Philadelphia, Pennsylvania, United States
• 3: Christopher C. Vogt , Garrison W. Cottrell, Fusion Via a Linear Combination of Scores, Information Retrieval, v.1 n.3, p.151-173, October 1999 [doi>10.1023/A:1009980820262]
• 4: E. Voorhees and D. Herman. Overview of the Eighth Text REtrieval Conference (TREC-8). In The Eighth Text REtrieval Conference (TREC- 8), 2000.

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

General Terms:
Design, Experimentation, Measurement, Performance, Reliability