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. © Springer-Verlag Berlin Heidelberg 2004.
|Title of host publication
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
|Number of pages
|Published - 2004
|International Conference on Intelligent Data Engineering and Automated Learning -
Duration: 1 Jan 1824 → …
|International Conference on Intelligent Data Engineering and Automated Learning
|1/01/24 → …