Probabilistic retrieval and coordination level matching

Robert Losee

Journal of the American Society for Information Science, 1987, vol. 38, issue 4, 239-244

Abstract: Probabilistic models of document‐retrieval systems incorporating sequential learning through relevance feedback may require frequent and time‐consuming reevaluations of documents. Coordination level matching is shown to provide equivalent document rankings to binary models when term discrimination values are equal for all terms; this condition may be found, for example, in probabilistic systems with no feedback. A nearest‐neighbor algorithm is presented which allows probabilistic sequential models consistent with two‐Poisson or binary‐independence assumptions to easily locate the “best” document using temporary sets of documents at a given coordination level. Conditions under which reranking is unnecessary are given. © 1987 John Wiley & Sons, Inc.

Date: 1987
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https://doi.org/10.1002/(SICI)1097-4571(198707)38:43.0.CO;2-6

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamest:v:38:y:1987:i:4:p:239-244

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