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

Edward Kai Fung Dang, Robert Wing Pong Luk and James Allan

Journal of the Association for Information Science & Technology, 2022, vol. 73, issue 8, 1140-1154

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.

Date: 2022
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https://doi.org/10.1002/asi.24619

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