User Profile Modeling and Applications to Digital Libraries
The ultimate goal of an information provider is to satisfy the user information needs. That is, to provide the user with the right information, at the right time, through the right means. A prerequisite for developing personalised services is to rely on user profiles representing users’ information needs. In this paper we will first address the issue of presenting a general user profile model. Then, the general user profile model will be customised for digital libraries users.
KeywordsData Category Digital Library Relevance Feedback User Information Collaborative Filter
Unable to display preview. Download preview PDF.
- 1.Altavista. Altavista Home Page: http://www.altavista.com, WWW.
- 2.Gianni Amati, Fabio Crestani, and Flavio Ubaldini. A learning system for selective dissemination of information. In Proc. of the 15th Int. Joint Conf. on Artificial Intelligence (IJCAI-97), pages 764–769, Nagoya, Japan, 1997.Google Scholar
- 3.Antonella Andreoni, Maria Bruna Baldacci, Stefania Biagioni, Carlo Carlesi, Donatella Castelli, Pasquale Pagano, and Carol Peters. Developing a European Thechnical Reference Digital Library. In Third European Conference on research and advanced technology for Digital Libraries, Paris, September 1999. Lecture Notes in Computer Science.Google Scholar
- 4.Krishna Bharat, Tomonari Kamba, and Michael Albers. Personalized, interactive news on the web. Multimedia Systems, (6):349–358, 1998.Google Scholar
- 5.Berkeley Workshop on Collaborative Filtering, 1996. http://www.sims.berkeley.edu/resources/collab/.
- 6.ACM Digital Library. ACM Digital Library Home Page: http://www.acm.org/dl, WWW.
- 7.Eurogatherer Telematics Information Engineering Project Number 8011. Eurogatherer Home Page: http://pc-erato2.iei.pi.cnr.it/eurogatherer/, WWW.
- 8.Larry Fitzpatrick and Mei Dent. Automatic feedback using past queries: Social searching? In Proceedings of the 20th International Conference on Research and Development in Information Retrieval (SIGIR-97), pages 306–313, Philadelphia, PA, July 1997.Google Scholar
- 9.A. Goker and T.L. McCluskey. Towards an adaptive information retrieval system. In Zbigniew W. Ras and Maria Zemenkova, editors, Proc. of the 6th Int. Sym. on Methodologies for Intelligent Systems (ISMIS-91), number 542 in Lecture Notes In Artificial Intelligence, pages 349–357. Springer-Verlag, 1991.Google Scholar
- 11.Toshiki Kindo, Hideyuki Yoshida, Tetsuro Morimoto, and Taisuke Watanabe. Adaptive personal information filtering system that organizes personal profiles automatically. In Proc. of the 15th Int. Joint Conf. on Artificial Intelligence (IJCAI-97), pages 716–721, Nagoya, Japan, 1997.Google Scholar
- 12.Medline. Medline Home Page: http://igm.nlm.nih.gov/, WWW.
- 13.NCSTRL. Networked Computer Science Technical Reference Library Home Page: http://www.ncstrl.org, WWW.
- 14.P3P. Platform for Privacy Preferences P3P Project Home Page: http://www.w3.org/P3P/, WWW.
- 15.Gerard Salton and J. Michael McGill. Introduction to Modern Information Retrieval. Addison Wesley Publ. Co., Reading, Massachussetts, 1989.Google Scholar
- 16.Amit Singhal and Mandar Mitra Buckley. Learning routing queries in a query zone. In Proceedings of the 20th International Conference on Research and Development in Information Retrieval (SIGIR-97), pages 25–32, Philadelphia, PA, July 1997.Google Scholar
- 17.Bienvenido Vélez, Ron Weiss, Mark Sheldon, and David K. Gifford. Fast and effective query refinement. In Proceedings of the 20th International Conference on Research and Development in Information Retrieval (SIGIR-97), pages 6–15, Philadelphia, PA, July 1997.Google Scholar