Measuring the likelihood property of scoring functions in general retrieval models

R. Bache, M. Baillie, F. Crestani

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Although retrieval systems based on probabilistic models will rank the objects (e.g., documents) being retrieved according to the probability of some matching criterion (e.g., relevance), they rarely yield an actual probability, and the scoring function is interpreted to be purely ordinal within a given retrieval task. In this brief communication, it is shown that some scoring functions possess the likelihood property, which means that the scoring function indicates the likelihood of matching when compared to other retrieval tasks, which is potentially more useful than pure ranking although it cannot be interpreted as an actual probability. This property can be detected by using two modified effectiveness measures: entire precision and entire recall.
Original languageEnglish
Pages (from-to)1294-1297
Number of pages4
JournalJournal of the American Society for Information Science and Technology
Volume60
Issue number6
DOIs
Publication statusPublished - 2009

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Keywords

  • information retrieval
  • probability
  • probabilistic models
  • retrieval systems

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