A unified maximum likelihood approach to document retrieval

David Bodoff, Daniel Enache, Ajit Kambil, Gary Simon, Alex Yukhimets

Research output: Contribution to journalArticlepeer-review


Empirical work shows significant benefits from using relevance feedback data to improve information retrieval (IR) performance. Still, one fundamental difficulty has limited the ability to fully exploit this valuable data. The problem is that it is not clear whether the relevance feedback data should be used to train the system about what the users really mean, or about what the documents really mean. In this paper, we resolve the question using a maximum likelihood framework. We show how all the available data can be used to simultaneously estimate both documents and queries in proportions that are optimal in a maximum likelihood sense. The resulting algorithm is directly applicable to many approaches to IR, and the unified framework can help explain previously reported results as well as guide the search for new methods that utilize feedback data in IR.

Original languageEnglish
Pages (from-to)785-796
Number of pages12
JournalJournal of the American Society for Information Science and Technology
Issue number10
StatePublished - Aug 2001
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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