An empirical testing of user stereotypes of information retrieval systems

Information Processing and Management

Volumn 41, Issue 3, 2005, Pages 651-664

  Zhang, Xiangmin ^a,b   Han, Hui ^a,b  

^a RUTGERS UNIVERSITY   (United States)

^b The Center for Nanotechnology Education and Utilization   (United States)

Author keywords

Information retrieval; Statistical analyses; Stereotype; User modeling

Indexed keywords

COMPUTATIONAL METHODS; DATABASE SYSTEMS; EVALUATION; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING; STATISTICAL METHODS;

EMPIRICAL TESTING; STEREOTYPE; SYSTEM PERFORMANCE; USER MODELING;

INFORMATION RETRIEVAL;

EID: 10644257817     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2004.01.005     Document Type: Article

References (44)

1. Allen, R. B. (1990). User models, theory, method, and practice. International Journal of Man-Machine Studies, 32, 511-543.
2. Allen, B. L. (1996). Information tasks: toward a user-centered approach to information systems. New York: Academic Press.
3. Bannister, D. (1968). The Evaluation of Personal Constructs. London and New York: Academic Press.
4. Beaumont, I. H. (1998). User modelling in the interactive anatomy tutoring system ANATOM-TUTOR. In P. Brusilovsky, A. Kobsa, J. Vassileva, (Eds.), Adaptive hypertext and hypermedia (pp. 91-115).
5. Bellika, J. G., Hartvigsen, G., & Widding, R. A. (1998). The virtual library secretary: a user model-based software agent. Personal Technologies, 2, 162-187.
6. Borgman, C. L. (1989). All users of information retrieval systems are not created equal: an exploration into individual Differences. Information Processing & Management, 25(3), 237-251.
7. Boyce, B. R., Meadow, C. T., & Kraft, D. H. (1994). Measurement in information science. San Diego: Academic Press.
8. Brajnik, G., Guida, G., & Tasso, C. (1990). User modeling in expert man-machine interfaces: a case study in intelligent information retrieval. IEEE Transactions on Systems, Man and Cybernetics, 20(1), 166-185.
9. Bushey, R., Manuney, J. M., & Deelman, T. (1999). The development of behavior-based user models for a computer system. In Proceedings of the seventh international conference on user modeling (pp. 109-118).
10. Charoenkitkarn, N. (1996). The effect of markup-querying on search pattern and performance in large-scale text retrieval. Ph.D. Dissertation. Department of Industrial Engineering, University of Toronto.
11. Chin, D. N. (1989). KNOME: modeling what the user knows in UC. In A. Kobsa, W. Wahlster, (Eds.), User models in dialog systems (pp. 35-51).
12. Ellis, D., Cox, D., & Hall, K. (1993). A comparison of the information seeking patterns of researchers in the physical and social sciences. Journal of Documentation, 49(4), 356-369.
13. Fernandez-Manjon, B., Fernandez-Valmayor, A., & Fernandez-Chamizo, C. (1998). Pragmatic user model implementation in an intelligent help system. British Journal of Educational Technology, 29(2), 113-123.
14. Fink, J., & Kobsa, A. (2000). A review and analysis of commercial user modeling servers for personalization on the World Wide Web. User Modeling and User-Adapted Interaction, 10, 209-249.
15. Fransella, F., & Bannister, D. (1977). A manual for repertory grid technique. London: Academic Press.
16. Harvey, C. F., Smith, P., & Lund, P. (1998). Providing a networked future for interpersonal information retrieval: InfoVine and user modeling. Interacting with Computers, 10, 195-212.
17. Huberty, C. (1994). Applied discriminant analysis. New York: John Wiley & Sons.
18. Kelly, G. A. (1955). The psychology of personal constructs. New York: Norton.
19. Kobsa, A., Muller, D., & Nul, A. (1994). KN-AHS: an adaptive hypertext client of the user modeling system BGP-MS. In Proceedings of the fourth international conference on user modeling (pp. 99-105).
20. Kuflik, T., Shapira, B., & Shoval, P. (2003). Stereotype-based versus personal-based filtering rules in information filtering systems. Journal of the American Society for Information Science and Technology, 54(3), 243-250.
21. Latta, G. F., & Swigger, K. (1992). Validation of the repertory grid for use in modelling knowledge. Journal of the American Society for Information Science, 43(2), 115-129.
22. Ma, W. (2002). A database selection expert system based on reference librarian's database selection strategy: a usability and empirical evaluation. Journal of the American Society for Information Science and Technology, 53(1), 567-580.
23. Mitchell, K., Woodbury, M. A., & Norcio, A. F. (1994). Individualizing user interfaces: application of the grade of membership (GoM) model for development of fuzzy user classes. Information Science, 1, 9-29.
24. Motaner, M., Lopez, B., & Lluis De La Rosa, J. (2003). A taxonomy of recommender agents on the Internet. Artificial Intelligence Review, 19, 285-330.
25. Mostafa, J., Quiroga, L. M., & Palakal, M. (1998). Filtering medical documents using automated and human classification methods. Journal of the American Society for Information Science, 49(14), 1304-1318.
26. Mulaik, S. A. (1972). The Foundations of factor analysis (p. 174). McGraw-Hill.
27. Neuman, D. (1995). High school students' use of databases: results of a national Delphi study. Journal of the American Society for Information Science, 46(4), 284-298.
28. Paterno, F., & Mancini, C. (2000). Effective levels of adaptation to different types of users in interactive museum systems. Journal of the American Society for Information Science, 51(1), 5-13.
29. Qiu, L. (1993). Markov models of search state patterns in a hypertext information retrieval system. Journal of the American Society for Information Science, 44(7), 413-427.
30. Rich, E. (1979). Users modeling via stereotypes. Cognitive Science, 3, 329-354.
31. Rich, E. (1989). Stereotypes and user modeling. In A. Kobsa & W. Wahlster (Eds.), User models in dialog systems (pp. 35-51). Berlin: Springer-Verlag.
32. Saracevic, T., & Kantor, P. (1988). A study of information seeking and retrieving: I. Background and methodology; III. Searchers, searches, overlap. Journal of the American Society for Information Science, 39(3), 161-176, 197-216.
33. SAS/STAT User's Guide, Release 6.03 Ed., SAS Institute Inc., Cary, NC, USA, 1988.
34. Shapira, B., Shoval, P., & Hanani, U. (1997). Stereotypes in information filtering systems. Information Processing and Management, 33(3), 273-287.
35. Shapira, B., Shoval, P., & Hanani, U. (1999). Experimentation with an information filtering system that combines cognitive and sociological filtering integrated with user stereotypes. Decision Support Systems, 27, 5-24.
36. Shaw, D. (1996). Undergraduate use of CD-ROM databases: human-computer interaction and relevance judgments. LISR (18), 261-274.
37. Shaw, M. L. G. (1980). On becoming a personal scientist. London: Academic Press.
38. Shaw, M. L. G., & Woodward, J. B. (1988). Validation in a knowledge support system: construing and consistency with multiple experts. International Journal of Man-Machine Studies, 29, 329-350.
39. Sollenborn, M., & Funk, P., (2002). Category-based filtering and user stereotype cases to reduce the latency problem in recommender systems. In Proceedings of the 6th European conference on case based reasoning (pp. 395-405).
40. Su, L. T. (2003). A comprehensive and systematic model of user evaluation of web search engines: II. An evaluation by undergraduates. Journal of the American Society for Information Science and Technology, 54(13), 1193-1223.
41. Voorhees, E. M. (2003). Overview of the TREC 2003 Question Answering Track. In Proceedings of TREC 2003 (pp. 14-27).
42. Yee, H. I. (1993). Effects of search experience and subject knowledge on the search tactics of novice and experienced searchers. Journal of the American Society for Information Science, 44(3), 161-174.
43. Zhang, X., & Chignell, M. (2001). Assessment of the effects of user characteristics on mental models of information retrieval systems. Journal of the American Society for Information Science and Technology, 52(6), 445-459.
44. Zhang, X., (2003). Discriminant analysis as a machine learning method for revision of user stereotypes of information retrieval systems. In S. A. Macskassy, et al. (Eds.), Workshop proceedings of machine learning, information retrieval and user modeling, 9th international conference on user modeling (pp. 1-10).