METIORE: A Personalized Information Retrieval System
- 705 Downloads
The idea of personalizing the interactions of a system is not new. With stereotypes the users are grouped into classes where all the users in a class have similar characteristics. Personalization was therefore not on individual basis but on a group of users. Personalized systems are also used in Intelligent Tutoring Systems (ITS) and in information filtering. In ITS, the pedagogical activities of a learner is personalized and in information filtering, the long-term stable information need of the user is used to filter incoming new information. We propose an explicit individual user model for representing the user’s activities during information retrieval. One of the new ideas here is that personalization is really individualized and linked with the user’s objective, that is his information need. Our proposals are implemented in the prototype METIORE for providing access to the publications in our laboratory. This prototype was experimented and we present in this paper the first results of our observation.
Unable to display preview. Download preview PDF.
- 1.Armstrong, R., Freitag, D., Joachims, T., & Mitchell, T. (1995). “Webwatcher: A learning apprentice for the world wide web”. AAAI Spring Symposium on Information Gathering from Heterogeneous Distributed EnvironmentsGoogle Scholar
- 2.Benaki, E., Karkaletsis, V.A., & Spyropoulos, C.D. (1997). “Integrating User Modeling Into Information Extraction: The UMIE Prototype”. UM’97. URL= http://um.org.
- 3.Billsus, D., & Pazzani, M. (1999). “A Hybrid User Model for News Story Classification”. Proceedings of the Seventh International Conference on User Modeling (UM’ 99) Banff, CanadaGoogle Scholar
- 4.Billsus, D., & Pazzani, M. (1999). “A Personal News Agent that Talks, Learns and Explains”. Proceedings of the Third International Conference on Autonomous Agents (Agents’ 99). URL= http://www.ics.uci.edu/~pazzani/Publications/agents99-news.pdf.
- 5.Brusilovsky, P., & Eklund, J. (1998). “A Study of User Model Based Link Annotation in Educational Hypermedia”. Journal of Universal Computer Science, 4(4), 429–448Google Scholar
- 6.Brusilovsky, P., Schwarz, E., & Weber, G. (1996). “ELM-ART: An intelligent tutoring system on World Wide Web”. Intelligent Tutoring Systems (pp. 261–269). BerlinIn Frasson, C., Gauthier, G., & Lesgold, A. (Eds.) Springer VerlagGoogle Scholar
- 8.Chin, D.N. (1989). “KNOME: Modeling What the User Knows in UC”. User Models in Dialog SystemsGoogle Scholar
- 9.Kamba T., & Bharat K. (1996). “An Interactive, Personalized Newspaper on WWW.” Multimedia Computing and Networking CaliforniaGoogle Scholar
- 10.Keogh, E., & Pazzani, M. (1999). “Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches.”. Uncertainty 99, 7th. Int’l Workshop on AI and Statistics, (pp. 225–230). Ft. Lauderdale, FloridaGoogle Scholar
- 11.Kobsa, A., Nill, A., & Dietmar Müller. (1996). “KN-AHN: An Adaptative Hypertext Client of the User Modeling System BGP-MS”. Review of Information Science 1(1). URL= http://www.inf-wiss.uni-konstanz/RIS.
- 12.Kononenko, I. (1990). “Comparison of Inductive and Naive Bayesian Learning Approaches to Automatic Knowledge Acquisition”. Current Trends in Knowledge Adquisition, 190–197Google Scholar
- 13.Lantz, A., & Kilander F. (1995). “Intelligent Filtering; Based on Keywords Only?”. Computer Human Interaction (CHI’95)Google Scholar
- 14.Mitchell, T., Caruana, R., McDermott, J., & Zabowski D. (1994). “Experience With a Learning Personal Assistant”. Communications of the ACM, 37(7)Google Scholar
- 15.Mitchell, T.M. (1997). “Machine Learning”. The McGraw-Hill Companies, Inc.Google Scholar
- 18.Schwab I., & Pohl W. (1999). “Learning Information Interest from Positive Examples”. User Modeling (UM’99)Google Scholar
- 19.Singh, M., & Provan, G.M. (1996). “Efficient learning of selective Bayesian network classifiers”.. Proceedings of the 13th International Conference on Machine LearningGoogle Scholar
- 20.Versteegen, L. (2000). “The Simple Bayesian Classifier as a Classification Algorithm”. URL= http://www.cs.kun.nl/nsccs/artikelen/leonv.ps.Z.
- 21.Weber, G., & Specht, M. (1997). “User modeling and adaptive navigation support in WWW-based tutoring systems”. (pp. 289–300). Wien Springer-VerlagGoogle Scholar