Learning user profiles for personalized information dissemination

Author(s): Tan, A. | Teo, C. |

Year: 1998

Citation: Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, Vol. 1, 183-188

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, called fuzzy ARAM (adaptive resonance associative map), can be used to learn user profiles for personalized information dissemination. ARAM learning is online, 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 (PIN). Preliminary experiments have verified that PIN is able to provide personalized news by adapting to user s interests in an online manner and generalizing them to new information on-the-fly

Topics: Machine Learning, Models: Fuzzy ARTMAP, Modified ART,

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