A Model for Personalized Web-Scale Case Base Maintenance

  • Authors:

  • Jingyu Sun;Xueli Yu;Ruizhi Wang;Ning Zhong

  • Affiliations:

  • College of Computer and Software, Taiyuan University of Technology, Taiyuan, China 030024 and International WIC Institute, Beijing University of Technology, Beijing, China 100022;College of Computer and Software, Taiyuan University of Technology, Taiyuan, China 030024;Dept. of Computer Science and Technology, Tongji University, Shanghai, China 200092;International WIC Institute, Beijing University of Technology, Beijing, China 100022 and Dept. of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan 371-0816

  • Venue:
  • AMT '09 Proceedings of the 5th International Conference on Active Media Technology
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

The growing use of case-based reasoning (CBR) systems on the Web has brought with it increased awareness of the Web-scale case base maintenance (CBM). While most existing CBM policies and approaches, which were designed for smaller case bases with sizes ranges from thousands to millions of cases, are not suitable for Web-scale distributed case collection. In this paper, we propose a novel CBM model for personalized Web-scale CBM, which addresses the needs of the Web-based CBR systems. The proposed CBM model is constructed based on chunk activation of ACT-R theory and rule-based reasoning. In addition, a basic activation value computation method is given to rank the cases and an algorithm is proposed to select top-N active cases. Experiments on several real-world datasets such as the MovieLens dataset show the effectiveness of our model.