Intelligent information-sharing systems
Communications of the ACM
Algorithms for clustering data
Algorithms for clustering data
Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Stereotypes in information filtering systems
Information Processing and Management: an International Journal
Information Filtering: A New Two-Phase Model Using StereotypicUser Profiling
Journal of Intelligent Information Systems - Special issue: next generation information technologies and systems
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A prototype system was developed to test the applicability of a dualmethod information-filtering model for filtering e-mail messages: content-based filtering and sociological filtering implemented with user stereotypes. This paper reports the main results of experiments that were run to determine the effects of combining the two methods in various ways. A major outcome of the experiments is that the combination of both methods yields better results than using each method individually. The optimal combination of the two filtering methods is stereotype dependent.