PNS: A Personalized News Aggregator on the Web

Part of the Studies in Computational Intelligence book series (SCI, volume 104)

This paper presents a system that aggregates news from various electronic news publishers and distributors. The system collects news from HTML and RSS Web documents by using source-specific information extraction programs (wrappers) and parsers, organizes them according to pre-defined news categories and constructs personalized views via a Web-based interface. Adaptive personalization is performed, based on the individual user interaction, user similarities and statistical analysis of aggregate usage data by machine learning algorithms. In addition to the presentation of the basic system, we present here the results of a user study, indicating the merits of the system, as well as ways to improve it further.


User Study News Item News Source Personalization Parameter Content Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    C.C. Aggarwal and P.S. Yu. An automated system for web portal personalization. In Proceedings of 28th International Conference on Very Large Data Bases (VLDB 2002), pages 1031–1040, 2002.Google Scholar
  2. 2.
    L. Ardissono, L. Console, and I. Torre. An adaptive system for the personalized access to news. AI Communications, 14(3):129–147, 2001.zbMATHGoogle Scholar
  3. 3.
    K. Bharat, T. Kamba, and M. Albers. Personalized, interactive news on the web. Multimedia Systems, 6(5):349–358, 1998.CrossRefGoogle Scholar
  4. 4.
    D. Billsus and M.J. Pazzani. A hybrid user model for news classification. In Proceedings of the International Conference on User Modeling (UM), CISM Courses and Lectures, n. 407, pages 99–108, 1999.Google Scholar
  5. 5.
    L. Chen and K.P. Sycara. Webmate: A personal agent for browsing and searching. In Proceedings of the Second International Conference on Autonomous Agents, pages 132–139, 1998.Google Scholar
  6. 6.
    P. Chesnais, M. Mucklo, and J. Sheena. The fishwrap personalized news system. In Proceedings of the IEEE 2nd International Workshop on Community Networking Integrating Multimedia Services to the Home, pages 275–282, 1995.Google Scholar
  7. 7.
    A. Diaz Esteban, M.J. Mana Lopez, M. de Buenaga Rodriguez, J.M. Gomez Hidalgo, and P.G. Gomez-Navarro. Using linear classifiers in the integration of user modeling and text content analysis in the personalization of a web-based spanish news service. In Proceedings of the Workshop on User Modeling, Machine Learning and Information Retrieval, 8th International Conference on User Modeling (UM2001), 2001.Google Scholar
  8. 8.
    T. Kamba, H. Sakagami, and Y. Koseki. Anatagonomy: a personalized newspaper on the world wide web. International Journal of Human-Computer Studies, 46(6):789–803, 1997.CrossRefGoogle Scholar
  9. 9.
    T. Kurki, S. Jokela, R. Sulonen, and M. Turpeinen. Agents in delivering personalized content based on semantic meta-data. In Proceedings of the AAAI Spring Symposium Workshop on Intelligent Agents in Cyberspace, pages 84–93, 1999.Google Scholar
  10. 10.
    G. Paliouras, V. Karkaletsis, C. Papatheodorou, and C.D. Spyropoulos. Exploiting learning techniques for the acquisition of user stereotypes and communities. In Proceedings of the International Conference on User Modeling (UM), CISM Courses and Lectures, n. 407, pages 169–178, 1999.Google Scholar
  11. 11.
    G. Paliouras, A. Mouzakidis, C. Ntoutsis, A. Alexopoulos, and C. Skourlas. Pns: Personalized multi-source news delivery. In Proceedings of the 10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems (KES), Lecture Notes in Artificial Intelligence, n. 4252, pages 1152–1161, 2006.Google Scholar
  12. 12.
    G. Paliouras, C. Papatheodorou, V. Karkaletsis, and C.D. Spyropoulos. Clustering the users of large web sites into communities. In Proceedings of the International Conference on Machine Learning (ICML), pages 719–726, 2000.Google Scholar
  13. 13.
    G. Paliouras, C. Papatheodorou, V. Karkaletsis, and C.D. Spyropoulos. Discovering user communities on the internet using unsupervised machine learning techniques. Interacting with Computers, 14(6):761–791, 2003.CrossRefGoogle Scholar
  14. 14.
    D. Pierrakos, G. Paliouras, C. Papatheodorou, and C.D. Spyropoulos. Web usage mining as a tool for personalization: a survey. User Modeling and User-Adapted Interaction, 13(4):311–372, 2003.CrossRefGoogle Scholar
  15. 15.
    G. Sigletos, G. Paliouras, C.D. Spyropoulos, and M. Hatzopoulos. Combining information extraction systems using voting and stacked generalization. Journal of Machine Learning Research, 6:1751–1782, 2005.MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Institute of Informatics and TelecommunicationsNational Center for Scientific Research “Demokritos”AthensGreece
  2. 2.Department of InformaticsTechnological Institute of AthensAthensGreece

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