Relevance thresholds: A multi-stage predictive model of how users evaluate information

Information Processing and Management

Volumn 39, Issue 3, 2003, Pages 403-423

  Greisdorf, Howard ^a  

^a UNIVERSITY OF NORTH TEXAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; HEURISTIC METHODS; MATHEMATICAL MODELS; PROBLEM SOLVING; STATISTICAL METHODS;

MULTI-STAGE PREDICTIVE MODEL;

INFORMATION RETRIEVAL;

EID: 0037408676     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4573(02)00032-8     Document Type: Article

References (42)

1. Arkes H.R., Hammond K.R. General introduction. Arkes H.R., Hammond K.R. Judgement and decision-making: an interdisciplinary reader. 1986;Cambridge University Press, Cambridge, MA.
2. Barry, C. L. (1993). The identification of user relevance criteria and document characteristics: Beyond the topical approach to information retrieval. Unpublished doctoral dissertation, Syracuse University, Syracuse, NY.
3. Barry C.L. User-defined relevance criteria: an exploratory study. Journal of the American Society for Information Science. 45(3):1994;149-159.
4. Barry C.L., Schamber L. Users' criteria for relevance evaluation: a cross-situational comparison. Information Processing and Management. 34(2 3):1998;219-236.
5. Blair D.C. Language and representation in information retrieval. 1990;Elsevier Science Publishing Company, New York, NY.
6. Blair D.C. STAIRS redux: thoughts on the STAIRS evaluation, ten years after. Journal of the American Society of Information Science. 47(1):1996;4-22.
7. Brooks T.A. People, words, and perceptions: a phenomenological investigation of textuality. Journal of the American Society for Information Science. 46(2):1995;103-115.
8. Brooks T.A. The relevance aura of bibliographic records. Information Processing and Management. 33(1):1997;69-80.
9. Cooper W.S. A definition of relevance for information retrieval. Information Storage and Retrieval. 7:1971;19-37.
10. Crestini F., Lalmas M., Van Rijsbergen C.J. Information retrieval: uncertainty and logics. 1998;Kluwer Academic Publishers, Boston, MA.
11. Ebenbach D.H., Moore C.F. Incomplete information, inferences, and individual differences: the case of environmental judgments. Organizational Behavior and Human Decision Processes. 81(1):2000;1-27.
12. Eisenberg M.B. Measuring relevance judgments. Information Processing and Management. 24(4):1988;373-389.
13. Gluck M. Exploring the relationship between user satisfaction and relevance in information systems. Information Processing and Management. 32(1):1996;89-104.
14. Greisdorf H., Spink A. Regions of relevance: approaches to measurement for enhanced precision. Proceedings of the 21st British computer society information retrieval sub group annual colloquium on IR research. 1994;1-33.
15. Greisdorf H., Spink A. Recent relevance research: implications for information professionals. Online Information Review. 24(5):2000;389-395.
16. Greisdorf H., Spink A. Median measure: an approach to IR system evaluation. Information Processing and Management. 37:2001;843-857.
17. Hinkle D., Wiersma W., Jurs S. Applied statistics for the behavioral sciences. 1994;Houghton Mifflin Company, Boston, MA.
18. Howard D.L. Pertinence as reflected in personal constructs. Journal of the American Society for Information Science. 45(3):1994;172-185.
19. Janes J.W. Other people's judgments: a comparison of users' and others' judgments of document relevance, topicality and utility. Journal of the American Society for Information Science. 45(3):1994;160-171.
20. Katter R.V. The influence of scale form on relevance judgments. Information Storage and Retrieval. 4:1968;1-11.
21. Maglaughlin K.L., Sonnewald D.H. User perspectives on relevance criteria: a comparison among relevant, partially relevant, and not-relevant judgments. Journal of the American Society for Information Science and Technology. 53(5):2002;327-342.
22. Montgomery H., Willen H. Decision making and action: the search for a good structure. Juslin P., Montgomery H. Judgment and decision making: Neo-Brunswickian and process-tracing approaches. 1999;147-173 Lawrence Erlbaum Associates Publishers, Mahwah, NJ.
23. Newell A. Unified theories of cognition. 1990;Harvard University Press, Cambridge, MA.
24. Park H. Relevance of science information: origins and dimensions of relevance and their implications to information retrieval. Information Processing and Management. 33(3):1997;339-352.
25. Saracevic T. A study of information seeking and retrieving. 1. Background and methodology. Journal of the American Society for Information Science. 39(3):1988;161-176.
26. Saracevic T. Relevane reconsidered '96. Second international conference on conceptions of library and information science: integration in perspective. 1996;201-216.
27. Saracevic T. Information science. Journal of the American Society for Information Science. 50(12):1999;1051-1063.
28. Schamber L. Users' criteria for evaluation in multimedia information seeking and use situations. ASIS '91: Proceedings of the American society for information science 54th annual meeting. 1991;126-133 Learned Information, Medford, NJ.
29. Schamber L. Relevance and information behavior. Martha E.W. Annual Review of Information Science and Technology (ARIST). Vol. 29:1994;3-48 Learned Information, Medford, NJ.
30. Sjoberg L. Attitudes, values and opinions: models and dynamics. Juslin P., Montgomery H. Judgment and decision making: Neo-Brunswickian and process-tracing approaches. 1999;219-241 Lawrence Erlbaum Associates Publishers, Mahwah, NJ.
31. Spink A., Greisdorf H., Bateman J. From highly relevant to nonrelevant: examining different regions of relevance. Information Processing and Management. 34(5):1998;599-622.
32. Spink A., Greisdorf H. Partial relevance judgments and changes in users information problems during online searching. Proceedings of the 18th national online meeting. 1997;323-334 Information Today, New York, NY.
33. Spink A., Greisdorf H. How and why end-users make relevance judgments. Proceedings of the 20th national online meeting. 1999;239-250 Information Today, New York, NY.
34. Spink A., Greisdorf H. Regions of relevance study: Part I. Measuring and mapping users' relevance judgments. Journal of the American Society for Information Science. 51(13):2000.
35. Spink A., Greisdorf H. Regions and levels: measuring and mapping users' relevance judgments. Journal of the American Society for Information Science and Technology. 52(2):2001;161-173.
36. Su L.T. The relevance of recall and precision in user evaluation. Journal of the American Society for Information Science. 1994;207-217.
37. Svenson O. Differentiation and consolidation theory: decision making processes before and after choice. Juslin P., Montgomery H. Judgment and decision making: Neo-Brunswickian and process-tracing approaches. 1999;175-197 Lawrence Erlbaum Associates, Publishers, Mahwah, NJ.
38. Tang R., Shaw W.M. Jr., Vevea J.L. Towards the identification of the optimal number of relevance categories. Journal of the American Society for Information Science. 50(3):1999;254-264.
39. Taube M. A note on the pseudo-mathematics of relevance. American Documentation. 16(2):1965;69-72.
40. Wallis P., Thom J.A. Relevance judgments for assessing recall. Information Processing and Management. 32(3):1996;273-286.
41. Wang P., Soergel D. Beyond topical relevance: document selection behavior of real users of IR systems. Proceedings of the 56th ASIS annual meeting. 1993;87-92 Learned Information, Medford, NJ.
42. Wilson P. Public knowledge, private ignorance. 1977;Greenwood Press, Westport, CT.