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A retrieval model family based on the probability ranking principle for ad hoc retrieval

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  • Edward Kai Fung Dang
  • Robert Wing Pong Luk
  • James Allan

Abstract

Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat‐B collection.

Suggested Citation

  • Edward Kai Fung Dang & Robert Wing Pong Luk & James Allan, 2022. "A retrieval model family based on the probability ranking principle for ad hoc retrieval," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1140-1154, August.
    • Handle: RePEc:bla:jinfst:v:73:y:2022:i:8:p:1140-1154
    DOI: 10.1002/asi.24619
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    References listed on IDEAS

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    1. Nicola Ferro & Gianmaria Silvello, 2018. "Toward an anatomy of IR system component performances," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 187-200, February.
    2. Javier Parapar & David E. Losada & Manuel A. Presedo‐Quindimil & Alvaro Barreiro, 2020. "Using score distributions to compare statistical significance tests for information retrieval evaluation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(1), pages 98-113, January.
    3. Arezki Hammache & Mohand Boughanem, 2021. "Term position‐based language model for information retrieval," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 627-642, May.
    4. S. E. Robertson & K. Sparck Jones, 1976. "Relevance weighting of search terms," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(3), pages 129-146, May.
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