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Capturing User Interests by Both Exploitation and Exploration

  • Ka Cheung Sia
  • Shenghuo Zhu
  • Yun Chi
  • Koji Hino
  • Belle L. Tseng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

Personalization is one of the important research issues in the areas of information retrieval and Web search. Providing personalized services that are tailored toward the specific preferences and interests of a given user can enhance her experience and satisfaction. However, to effectively capture user interests is a challenging research problem. Some challenges include how to quickly capture user interests in an unobtrusive way, how to provide diversified recommendations, and how to track the drifts of user interests in a timely fashion. In this paper, we propose a model for learning user interests and an algorithm that actively captures user interests through an interactive recommendation process. The key advantage of our algorithm is that it takes into account both exploitation (recommending items that belong to users’ core interest) and exploration (discovering potential interests of users). Extensive experiments using synthetic data and a user study show that our algorithm can quickly capture diversified user interests in an unobtrusive way, even when the user interests may drift along time.

Keywords

User Study Query Expansion User Interest Important Research Issue Exploration Bonus 
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 2007

Authors and Affiliations

  • Ka Cheung Sia
    • 1
  • Shenghuo Zhu
    • 2
  • Yun Chi
    • 2
  • Koji Hino
    • 2
  • Belle L. Tseng
    • 2
  1. 1.University of California, Los Angeles, CA 90095USA
  2. 2.NEC Laboratories America, 10080 N. Wolfe Rd, SW3-350, Cupertino, CA 95014USA

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