Personalized News Reading via Hybrid Learning
| Autor: | Sunny Yeung, Ke Chen |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Scheme (programming language)
Information retrieval Computer science business.industry media_common.quotation_subject Supervised learning Order (business) Hybrid system Adaptive system Reading (process) Unsupervised learning Artificial intelligence business computer media_common computer.programming_language |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540228813 IDEAL |
| DOI: | 10.1007/978-3-540-28651-6_89 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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