Dynamic Composition of Information Retrieval Techniques

  • Authors:
  • Andrew Arnt;Shlomo Zilberstein;James Allan;Abdel-Illah Mouaddib

  • Affiliations:
  • Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA. arnt@cs.umass.edu;Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA. shlomo@cs.umass.edu;Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA. allan@cs.umass.edu;Département d'informatique, Université de Caen, 14032 Caen Cedex, France. mouaddib@info.unicaen.fr

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2004

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Abstract

This paper presents a new approach to information retrieval (IR) based on run-time selection of the best set of techniques to respond to a given query. A technique is selected based on its projected effectiveness with respect to the specific query, the load on the system, and a time-dependent utility function. The paper examines two fundamental questions: (1) can the selection of the best IR techniques be performed at run-time with minimal computational overhead? and (2) is it possible to construct a reliable probabilistic model of the performance of an IR technique that is conditioned on the characteristics of the query? We show that both of these questions can be answered positively. These results suggest a new system design that carries a great potential to improve the quality of service of future IR systems.