A retrieval model family based on the probability ranking principle for ad hoc retrieval
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, cal...
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| Veröffentlicht in: | Journal of the American Society for Information Science and Technology 2022-08, Vol.73 (8), p.1140-1154 |
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| container_end_page | 1154 |
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| container_issue | 8 |
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| container_title | Journal of the American Society for Information Science and Technology |
| container_volume | 73 |
| creator | Dang, Edward Kai Fung Luk, Robert Wing Pong Allan, James |
| description | 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. |
| doi_str_mv | 10.1002/asi.24619 |
| format | Article |
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| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete; Business Source Complete |
| subjects | Documents Families & family life Principles Ranking Retrieval Statistical analysis |
| title | A retrieval model family based on the probability ranking principle for ad hoc retrieval |
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