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What Happened to Content-Based Information Filtering?

  • Nikolaos Nanas
  • Anne De Roeck
  • Manolis Vavalis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5766)

Abstract

Personalisation can have a significant impact on the way information is disseminated on the web today. Information Filtering can be a significant ingredient towards a personalised web. Collaborative Filtering is already being applied successfully for generating personalised recommendations of music tracks, books, movies and more. The same is not true for Content-Based Filtering. In this paper, we identify some possible reasons for the notable absence of a broad range of personalised information delivery and dissemination services on the web today. We advocate that a more holistic approach to user profiling is required and we discuss the series of still open, challenging research issues raised.

Keywords

Recommender System Collaborative Filter Information Item User Interest Personalise Recommendation 
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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References

  1. 1.
    Amati, G., D’ Aloisi, D., Giannini, V., Ubaldini, F.: A framework for filtering news and managing distributed data. Journal of Universal Computer Science 3(8), 1007–1021 (1997)Google Scholar
  2. 2.
    Balabanovic, M., Shoham, Y.: Combining content-based and collaborative recommendation. Communications of the ACM 40, 66–72 (1997)CrossRefGoogle Scholar
  3. 3.
    Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  4. 4.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Konstan, J.A., Riedl, J., Borchers, A., Herlocker, J.L.: Recommender systems: A grouplens perspective. In: Recommender Systems. Papers from 1998 Workshop. Technical Report WS-98-08, pp. 60–64. AAAI Press, Menlo Park (1998)Google Scholar
  7. 7.
    Mladenic, D.: Using text learning to help web browsing. In: 9th International Conference on Human-Computer Interaction (HCI International 2001), New Orleans, LA, pp. 893–897 (2001)Google Scholar
  8. 8.
    Nanas, N., De Roeck, A.: Multimodal dynamic optimisation: from evolutionary algorithms to artificial immune systems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 13–24. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Nanas, N., De Roeck, A.: A review of evolutionary and immune inspired information filtering. Natural Computing (2007)Google Scholar
  10. 10.
    Nanas, N., Vavalis, M.: A “bag” or a “window” of words for information filtering. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 182–193. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Pon, R.K., Cárdenas, A.F., Buttler, D.J.: Online selection of parameters in the rocchio algorithm for identifying interesting news articles. In: WIDM 2008: Proceeding of the 10th ACM workshop on Web information and data management, pp. 141–148. ACM, New York (2008)Google Scholar
  12. 12.
    Seo, Y., Zhang, B.: A reinforcement learning agent for personalized information filtering. In: Intelligent User Interfaces, New Orleans, LA, pp. 248–251 (2000)Google Scholar
  13. 13.
    Webb, G.I., Pazzani, M.J., Billsus, D.: Machine learning for user modeling. User Modeling and User-Adapted Interaction 11, 19–29 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Wermter, S.: Neural networks agents for learning semantic text classification. Information Retrieval 3, 87–103 (2000)CrossRefGoogle Scholar
  15. 15.
    Widyantoro, D.H., Ioerger, T.R., Yen, J.: An adaptive algorithm for learning changes in user interests. In: ACM/CIKM 1999 Conference on Information and Knowledge Management, Kansas City, MO, pp. 405–412 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nikolaos Nanas
    • 1
  • Anne De Roeck
    • 2
  • Manolis Vavalis
    • 1
  1. 1.Laboratory for Information Systems and ServicesCentre for Research and Technology - Thessaly (CERETETH)Greece
  2. 2.Computing DepartmentThe Open UniversityUK

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