A generic ranking function discovery framework by genetic programming for information retrieval

  • Authors:

  • Weiguo Fan;Michael D. Gordon;Praveen Pathak

  • Affiliations:

  • Department of Accounting and Information Systems, Virginia Polytechnic Institute and State University, Blacksburg, VA;Department of Computer and Information Systems, University of Michigan, Ann Arbor, MI;Department of Decision and Information Sciences, University of Florida, Gainesville, FL

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2004

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Abstract

Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.