Journal article

Improved collaborative filtering algorithm via information transformation

  • Liu, Jian-Guo Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai, China - Department of Modern Physics, University of Science and Technology of China, Hefei, China - Department of Physics, University of Fribourg, Switzerland
  • Wang, Bing-Hong Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai, China - Department of Modern Physics, University of Science and Technology of China, Hefei, China - Department of Physics, University of Fribourg, Switzerland
  • Guo, Qiang Dalian Nationalities University, Dalian, China
    2009
Published in:
  • International Journal of Modern Physics C. - 2009, vol. 20, no. 2, p. 285-293
English In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user–user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301490
Statistics

Document views: 6 File downloads:
  • liu_icf.pdf: 1