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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2004: Intelligent Data Engineering and Automated Learning – IDEAL 2004 pp 607–612Cite as

Personalized News Reading via Hybrid Learning

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3177))

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.

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References

  1. Levy, Y.A., Weld, D.S.: Intelligent Internet Systems. Artificial Intelligence 118(1), 1–14 (2000)

    Article  Google Scholar 

  2. Witten, H.I., Bell, T.C.: The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory 37(4), 545–558 (1991)

    Article  Google Scholar 

  3. Craven, M., et al.: Learning to construct knowledge bases from the WWW. Artificial Intelligence 118(1), 69–113 (2000)

    Article  MATH  Google Scholar 

  4. Chen, K.: Towards better making a decision in speaker verification. Pattern Recognition 36(2), 329–346 (2003)

    Article  Google Scholar 

  5. Yeung, S.: A Personalized New Reading System, MSc Thesis, School for Computer Science, The University of Birmingham, U.K (2002)

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  6. Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Informatics, The University of Manchester, Manchester, M60 1QD, UK

    Ke Chen

  2. School of Computer Science, Birmingham University, Birmingham, B15 2TT, UK

    Sunny Yeung

Authors
  1. Ke Chen
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  2. Sunny Yeung
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Editor information

Editors and Affiliations

  1. School of Engineering, Computing, and Mathematics, University of Exeter, EX4 4QF, Exeter, UK

    Zheng Rong Yang

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. School of Engineering, Computer Science and Mathematics, University of Exeter, EX4 4QF, UK

    Richard M. Everson

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© 2004 Springer-Verlag Berlin Heidelberg

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Cite this paper

Chen, K., Yeung, S. (2004). Personalized News Reading via Hybrid Learning. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_89

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  • DOI: https://doi.org/10.1007/978-3-540-28651-6_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22881-3

  • Online ISBN: 978-3-540-28651-6

  • eBook Packages: Springer Book Archive

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