Learning user profiles for personalized information dissemination

• Main Authors: TAN, Ah-hwee, TEO, Christine
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Adaptive resonance associative map (ARAM); Adaptive resonance theory (ART); Personalized information network (PIN); Databases and Information Systems; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/6452; https://ink.library.smu.edu.sg/context/sis_research/article/7455/viewcontent/pin_ijcnn98.pdf
• Institution: Singapore Management University

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.