IDEAL 2004: Intelligent Data Engineering and Automated Learning – IDEAL 2004 pp 607-612 | Cite as
Personalized News Reading via Hybrid Learning
Abstract
In this paper, we present a personalized news reading prototype where latest news articles published by various on-line news providers are automatically collected, categorized and ranked in light of a user’s habits or interests. Moreover, our system can adapt itself towards a better performance. In order to develop such an adaptive system, we proposed a hybrid learning strategy; supervised learning is used to create an initial system configuration based on user’s feedbacks during registration, while an unsupervised learning scheme gradually updates the configuration by tracing the user’s behaviors as the system is being used. Simulation results demonstrate satisfactory performance.
Keywords
Supervise Learning Unsupervised Learning News Article Personalized News News ReadingPreview
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