Validation in a knowledge support system: construing and consistency with multiple experts
International Journal of Man-Machine Studies
SAS-STAT User's Guide: release 6.03 edition
SAS-STAT User's Guide: release 6.03 edition
Information Processing and Management: an International Journal
User models: theory, method, and practice
International Journal of Man-Machine Studies
Markov models of search state patterns in a hypertext information retrieval system
Journal of the American Society for Information Science
Information Sciences—Applications: An International Journal
High school students' use of databases: results of a national Delphi study
Journal of the American Society for Information Science
Stereotypes in information filtering systems
Information Processing and Management: an International Journal
Filtering medical documents using automated and human classification methods
Journal of the American Society for Information Science
The development of behavior-based user models for a computer system
UM '99 Proceedings of the seventh international conference on User modeling
Effective levels of adaptation to different types of users in interactive museum systems
Journal of the American Society for Information Science - Special topic issue: When museum informatics meets the World Wide Web
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Assessment of the effects of user characteristics on mental models of information retrieval systems
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Information Tasks: Toward a User-Centered Approach to Information Systems
Information Tasks: Toward a User-Centered Approach to Information Systems
On Becoming a Personal Scientist: Interactive Computer Elicitation of Personal Models of the World
On Becoming a Personal Scientist: Interactive Computer Elicitation of Personal Models of the World
A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web
User Modeling and User-Adapted Interaction
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Stereotype-based versus personal-based filtering rules in information filtering systems
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Examining user resistance and management strategies in enterprise system implementations
Proceedings of the 2007 ACM SIGMIS CPR conference on Computer personnel research: The global information technology workforce
Effective tag mechanisms for evolving coordination
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Personalized Teaching of a Programming language over the web: Stereotypes and rule-based mechanisms
Proceedings of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
Supporting information spread in a social internetworking scenario
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Stereotyping is a technique used in many information systems to represent user groups and/or to generate initial individual user models. However, there has been a lack of evidence on the accuracy of their use in representing users. We propose a formal evaluation method to test the accuracy or homogeneity of the stereotypes that are based on users' explicit characteristics. Using the method, the results of an empirical testing on 11 common user stereotypes of information retrieval (IR) systems are reported. The participants' memberships in the stereotypes were predicted using discriminant analysis, based on their IR knowledge. The actual membership and the predicted membership of each stereotype were compared. The data show that "librarians/IR professionals" is an accurate stereotype in representing its members, while some others, such as "undergraduate students" and "social sciences/humanities" users, are not accurate stereotypes. The data also demonstrate that based on the user's IR knowledge a stereotype can be made more accurate or homogeneous. The results show the promise that our method can help better detect the differences among stereotype members, and help with better stereotype design and user modeling. We assume that accurate stereotypes have better performance in user modeling and thus the system performance.Limitations and future directions of the study are discussed.