Name
Abstract | ||
|---|---|---|
The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of two similarly performing systems is superior. For both a single query term and for a multiple query term retrieval model, a method for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering are described. Simulations illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1016/0306-4573(95)00072-O | Inf. Process. Manage. |
Keywords | Field | DocType |
information retrieval,inverse document frequency | Divergence-from-randomness model,Data mining,Relevance feedback,Information retrieval,Query expansion,tf–idf,Computer science,Term Discrimination,Document retrieval,Probabilistic logic,Vector space model | Journal |
Volume | Issue | ISSN |
31 | 4 | Information Processing and Management |
Citations | PageRank | References |
9 | 0.71 | 25 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert M. Losee | 1 | 276 | 36.01 |