User Preference Modeling Based on Interest and Impressions for News Portal Site Systems

  • Yukiko Kawai
  • Tadahiko Kumamoto
  • Katsumi Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


We have developed an application called My Portal Viewer (MPV) [1] that effectively integrates many articles collected from multiple news sites and presents these integrations through a familiar interface such as a page the user has often visited. MPV dynamically determines keywords of interest that a user might potentially be interested in based on the history of the articles the user has read and creates categories based on these interest words. MPV and many other similar integration systems, however, cause problems where users cannot find only their interest articles in each category because they are only ranked by frequency and the cooccurrence of keywords. We propose a new method of selecting further articles from each category using a user’s impressions of articles. The improved MPV, called MPV Plus, selects and recommends more desirable articles using the method we propose. This paper presents the design concept and process flow of MPV Plus and reports on its effectiveness as evaluated in experiments.


User Preference News Article Original Category Integrate Category Impression Word 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yukiko Kawai
    • 1
  • Tadahiko Kumamoto
    • 2
  • Katsumi Tanaka
    • 3
  1. 1.Undergraduate School of ScienceKyoto Sangyo UniversityKyoto-CityJapan
  2. 2.National Institute of Information and Communications TechnologyKyotoJapan
  3. 3.Graduate School of InformaticsKyoto UniversityKyotoJapan

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