A retrieval model family based on the probability ranking principle for ad hoc retrieval
Edward Kai Fung Dang, Robert W. P. Luk, James Allan · Journal of the Association for Information Science and Technology · 2022
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.