Learning user profiles for personalized information dissemination
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 info...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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