When is the probability ranking principle suboptimal?
The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. Gordon and Lenk (1991) demonstrated that this principal is optimal within a signal detection—decision theory framework, and it maximizes the inquirer's expected utility for...
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| Published in | Journal of the American Society for Information Science Vol. 43; no. 1; pp. 1 - 14 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Washington, D.C
Wiley Subscription Services, Inc., A Wiley Company
01.01.1992
John Wiley & Sons American Documentation Institute Wiley Periodicals Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0002-8231 1097-4571 |
| DOI | 10.1002/(SICI)1097-4571(199201)43:1<1::AID-ASI1>3.0.CO;2-5 |
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| Abstract | The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. Gordon and Lenk (1991) demonstrated that this principal is optimal within a signal detection—decision theory framework, and it maximizes the inquirer's expected utility for relevant documents. These results hold under three conditions: calibration, independent assessment of relevance by the inquirer, and certainty about the computed probabilities of relevance. We demonstrate that the probability ranking principle can be suboptimal with respect to expected utility when one of these conditions fails to hold. © 1992 John Wiley & Sons, Inc. |
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| AbstractList | The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. This principal is optimal within a signal detection-decision theory framework, and it maximises the inquirer's expected utility for relevant documents. These results hold under 3 conditions: calibration; independent assessment of relevance by the inquirer; and certainty about the computed probabilities of relevance. The probability ranking principle can be suboptimal with respect to expected utility when one of these conditions fails to hold. 00 Original abstract The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. Gordon and Lenk (1991) demonstrated that this principal is optimal within a signal detection—decision theory framework, and it maximizes the inquirer's expected utility for relevant documents. These results hold under three conditions: calibration, independent assessment of relevance by the inquirer, and certainty about the computed probabilities of relevance. We demonstrate that the probability ranking principle can be suboptimal with respect to expected utility when one of these conditions fails to hold. © 1992 John Wiley & Sons, Inc. The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. It is demonstrated that this principal is optimal within a signal detection-decision theory framework and that it maximizes the inquirer's expected utility for relevant documents. These results hold under 3 conditions: 1. calibration, 2. independent assessment of relevance by the inquirer, and 3. certainty about the computed probabilities of relevance. It is shown that the probability ranking principle can be suboptimal with respect to expected utility when one of these conditions fails to hold. Discussion of probabilistic information retrieval systems highlights the probability ranking principle and discusses when the standard retrieval policy is optimal. Topics discussed include calibration and refinement; independent assessment of relevance by the inquirer; certainty about the computed probabilities of relevance; and confidence and highest predictive distribution (HPD) intervals. (23 references) (LRW) The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. Gordon and Lenk (1991) demonstrated that this principal is optimal within a signal detection--deci sion theory framework, and it maximizes the inquirer's expected utility for relevant documents. These results hold under three conditions: calibration, independent assessment of relevance by the inquirer, and certainty about the computed probabilities of relevance. We demonstrate that the probability ranking principle can be suboptimal with respect to expected utility when one of these conditions fails to hold. |
| Author | Gordon, Michael D. Lenk, Peter |
| Author_xml | – sequence: 1 givenname: Michael D. surname: Gordon fullname: Gordon, Michael D. organization: School of Business Administration, The University of Michigan, Ann Arbor, MI 48109-1234 – sequence: 2 givenname: Peter surname: Lenk fullname: Lenk, Peter organization: School of Business Administration, The University of Michigan, Ann Arbor, MI 48109-1234 |
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| Copyright | Copyright © 1992 John Wiley & Sons, Inc. 1992 INIST-CNRS Copyright Wiley Periodicals Inc. Jan 1992 |
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| DOI | 10.1002/(SICI)1097-4571(199201)43:1<1::AID-ASI1>3.0.CO;2-5 |
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| Keywords | Condition Relevance Document retrieval Document retrieval system Decision theory Prediction Probabilistic model Statistical decision Optimization |
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| References | Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78, 1-3. Tague, J. M. (1973). A Bayesian approach to information retrieval. Information Storage and Retrieval, 9, 129-142. Berger, J. O. (1985). Statistical decision theory and Bayesian analysis. New York: Springer Verlag. Cox, D. R. (1970). The analysis of binary data. London: Methuen. Savage, J. L. (1971). The elicitation of personal probabilities. Journal of the American Statistical Association, 77, 783-801. Robertson, S. E. (1977). The probability ranking principle in IR. Journal of Documentation, 33, 294-304. Eisenberg, M., & Barry, C. (1988). Order effects: A study of the possible influence of presentation order on user judgements of document relevance. Journal of the American Society for Information Science, 39, 293-300. DeGroot, M. H., & Fienberg, S. H. (1983). The comparison and evaluation of forecasters. The Statistician, 32, 12-22. Gordon, M. (1991). Ranking large document collections by a state space search. Information Processing and Management, Vol. 27, No. 1, pp. 27-41. Lenk, P., & Floyd, B. (1988). Dynamically updating relevance judgements in probabilistic information systems via users' feedback. Management Science, 34, 1450-1459. Van Rijsbergen, C. J. (1979). Information retrieval (2nd ed.). London: Butterworths. Kochen, M. (1974). Principles of information retrieval. Los Angeles: Melville Publishing. Bookstein, A. (1977). When the most 'pertinent' document should not be retrieved-an analysis of the Swets' model. Journal of the American Society for Information Science, 13, 377-383. Bickel, P. J., & Doksum, K. A. (1977). Mathematical statistics. San Francisco: Holden-Day. Maron, M. F., & Kuhns, J. L. (1960). On relevance, probabilistic indexing, and information retrieval. Journal of ACM, 7, 216-244. Fuhr, N., & Hüther, H. (1989). Optimum probability estimation from empirical distributions. Information Processing & Management, 25, 493-507. Robertson, S. E., & Sparck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Science, 27, 129-146. Gordon, M., & Lenk, P. (1991). A utility theoretic examination of the probability ranking principle in information retrieval. Journal of the American Society for Information Science, 42, 703-714. Losee, R. M. (1988). Parameter estimation for probabilistic document retrieval models. Journal of the American Society for Information Science, 39, 8-16. Bookstein, A. (1983). Information retrieval: A sequential learning process. Journal of the American Society for Information Science, 34, 331-342. 1971; 77 1950; 78 1991; 27 1973; 9 1982; 1 1991; 42 1988; 39 1983; 32 1985 1974 1988; 34 1972 1977; 33 1960; 7 1977; 13 1970 1976; 27 1989; 25 1979 1977 1983; 34 |
| References_xml | – reference: Bickel, P. J., & Doksum, K. A. (1977). Mathematical statistics. San Francisco: Holden-Day. – reference: Bookstein, A. (1983). Information retrieval: A sequential learning process. Journal of the American Society for Information Science, 34, 331-342. – reference: Robertson, S. E., & Sparck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Science, 27, 129-146. – reference: Tague, J. M. (1973). A Bayesian approach to information retrieval. Information Storage and Retrieval, 9, 129-142. – reference: Lenk, P., & Floyd, B. (1988). Dynamically updating relevance judgements in probabilistic information systems via users' feedback. Management Science, 34, 1450-1459. – reference: Berger, J. O. (1985). Statistical decision theory and Bayesian analysis. New York: Springer Verlag. – reference: DeGroot, M. H., & Fienberg, S. H. (1983). The comparison and evaluation of forecasters. The Statistician, 32, 12-22. – reference: Van Rijsbergen, C. J. (1979). Information retrieval (2nd ed.). London: Butterworths. – reference: Gordon, M. (1991). Ranking large document collections by a state space search. Information Processing and Management, Vol. 27, No. 1, pp. 27-41. – reference: Bookstein, A. (1977). When the most 'pertinent' document should not be retrieved-an analysis of the Swets' model. Journal of the American Society for Information Science, 13, 377-383. – reference: Fuhr, N., & Hüther, H. (1989). Optimum probability estimation from empirical distributions. Information Processing & Management, 25, 493-507. – reference: Gordon, M., & Lenk, P. (1991). A utility theoretic examination of the probability ranking principle in information retrieval. Journal of the American Society for Information Science, 42, 703-714. – reference: Maron, M. F., & Kuhns, J. L. (1960). On relevance, probabilistic indexing, and information retrieval. Journal of ACM, 7, 216-244. – reference: Eisenberg, M., & Barry, C. (1988). Order effects: A study of the possible influence of presentation order on user judgements of document relevance. Journal of the American Society for Information Science, 39, 293-300. – reference: Kochen, M. (1974). Principles of information retrieval. Los Angeles: Melville Publishing. – reference: Savage, J. L. (1971). The elicitation of personal probabilities. Journal of the American Statistical Association, 77, 783-801. – reference: Losee, R. M. (1988). Parameter estimation for probabilistic document retrieval models. Journal of the American Society for Information Science, 39, 8-16. – reference: Cox, D. R. (1970). The analysis of binary data. London: Methuen. – reference: Robertson, S. E. (1977). The probability ranking principle in IR. Journal of Documentation, 33, 294-304. – reference: Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78, 1-3. – year: 1985 – volume: 25 start-page: 493 year: 1989 end-page: 507 article-title: Optimum probability estimation from empirical distributions publication-title: Information Processing & Management – volume: 42 start-page: 703 year: 1991 end-page: 714 article-title: A utility theoretic examination of the probability ranking principle in information retrieval publication-title: Journal of the American Society for Information Science – volume: 33 start-page: 294 year: 1977 end-page: 304 article-title: The probability ranking principle in IR publication-title: Journal of Documentation – volume: 39 start-page: 8 year: 1988 end-page: 16 article-title: Parameter estimation for probabilistic document retrieval models publication-title: Journal of the American Society for Information Science – volume: 27 start-page: 27 issue: 1 year: 1991 end-page: 41 article-title: Ranking large document collections by a state space search publication-title: Information Processing and Management – volume: 78 start-page: 1 year: 1950 end-page: 3 article-title: Verification of forecasts expressed in terms of probability publication-title: Monthly Weather Review – volume: 7 start-page: 216 year: 1960 end-page: 244 article-title: On relevance, probabilistic indexing, and information retrieval publication-title: Journal of ACM – volume: 34 start-page: 331 year: 1983 end-page: 342 article-title: Information retrieval: A sequential learning process publication-title: Journal of the American Society for Information Science – volume: 27 start-page: 129 year: 1976 end-page: 146 article-title: Relevance weighting of search terms publication-title: Journal of the American Society for Information Science – volume: 9 start-page: 129 year: 1973 end-page: 142 article-title: A Bayesian approach to information retrieval publication-title: Information Storage and Retrieval – year: 1972 – volume: 32 start-page: 12 year: 1983 end-page: 22 article-title: The comparison and evaluation of forecasters publication-title: The Statistician – year: 1974 – year: 1970 – volume: 34 start-page: 1450 year: 1988 end-page: 1459 article-title: Dynamically updating relevance judgements in probabilistic information systems via users' feedback publication-title: Management Science – volume: 13 start-page: 377 year: 1977 end-page: 383 article-title: When the most 'pertinent' document should not be retrieved—an analysis of the Swets' model publication-title: Journal of the American Society for Information Science – year: 1979 – year: 1977 – volume: 39 start-page: 293 year: 1988 end-page: 300 article-title: Order effects: A study of the possible influence of presentation order on user judgements of document relevance publication-title: Journal of the American Society for Information Science – volume: 77 start-page: 783 year: 1971 end-page: 801 article-title: The elicitation of personal probabilities publication-title: Journal of the American Statistical Association – volume: 1 start-page: 291 year: 1982 end-page: 314 |
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| Snippet | The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. Gordon and Lenk (1991) demonstrated... Discussion of probabilistic information retrieval systems highlights the probability ranking principle and discusses when the standard retrieval policy is... The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. This principal is optimal within a... The probability ranking principle retrieves documents in decreasing order of their predictive probabilities of relevance. It is demonstrated that this... |
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| SubjectTerms | Calibration Confidence Intervals (Statistics) Decision theory Exact sciences and technology Expected utility Information and communication sciences Information processing and retrieval Information Retrieval Information retrieval systems. Information and document management system Information science Information science. Documentation Information storage Information storage and retrieval Information work Mathematical Formulas Parameter estimation Probabilistic indexing Probabilistic Models Probability Ranking Relevance Relevance (Information Retrieval) Science Sciences and techniques of general use Searching Signal detection Subject indexing Technical services User Satisfaction (Information) |
| Title | When is the probability ranking principle suboptimal? |
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