Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2010
Abstract
Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised Adaptive Resonance Theory (ART) system, known as fuzzy ARAM, can be used to learn user profiles for personalized information dissemination. ARAM learning is on-line, fast, and incremental. Acquisition of new knowledge does not require re-training on previously learned cases. ARAM integrates both user-defined and system-learned knowledge in a single framework. Therefore inconsistency between the two knowledge sources will not arise. ARAM has been used to develop a personalized news system known as PIN. Preliminary experiments have verified that PIN is able to provide personalized news by adapting to user's interests in an on-line manner and generalizing to new information on-the-fly.
Keywords
Adaptive resonance associative map (ARAM), Adaptive resonance theory (ART), Personalized information network (PIN)
Discipline
Databases and Information Systems | Information Security
Research Areas
Data Science and Engineering
Publication
Proceedings of the International Joint Conference on Neural Networks (IJCNN '98): Alaska, May 4-9
First Page
183
Last Page
188
Publisher
AAAi Press
City or Country
Palo Alto, CA
Citation
TAN, Ah-hwee and TEO, Christine.
Learning user profiles for personalized information dissemination. (2010). Proceedings of the International Joint Conference on Neural Networks (IJCNN '98): Alaska, May 4-9. 183-188.
Available at: https://ink.library.smu.edu.sg/sis_research/6452
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.