An analytic method of information retrieval and filtering evaluation can quantitatively predict the expected number of documents examined in retrieving a relevant document. It also allows researchers and practitioners to qualitatively understand how varying different estimates of query parameter values affects retrieval performance. The incorporation of relevance feedback to increase our knowledge about the parameters of relevant documents and the robustness of parameter estimates are modeled. Single term and two term independence models, as well as a complete term dependence model, are developed. An economic model of retrieval performance may be used to study the effects of database size and to provide analytic answers to questions comparing retrieval from small and large databases, as well as questions about the number of terms in a query. Results are presented as a performance surface, a three dimensional graph showing the effects of two independent variables on performance.
This material is further discussed in Text Retrieval and Filtering: Analytic Models of Performance, Boston: Kluwer, 1998.
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