Journal article

Information filtering via weighted heat conduction algorithm

  • Liu, Jian-Guo Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai, China - CABDyN Complexity Centre, Säid Business School, University of Oxford, United Kingdom
  • Guo, Qiang Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai, China
  • Zhang, Yi-Cheng Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai, China - Department of Physics, University of Fribourg, Switzerland
    02.03.2011
Published in:
  • Physica A: Statistical Mechanics and its Applications. - 2011, vol. 390, no. 12, p. 2414-2420
English In this paper, by taking into account effects of the user and object correlations on a heat conduction (HC) algorithm, a weighted heat conduction (WHC) algorithm is presented. We argue that the edge weight of the user–object bipartite network should be embedded into the HC algorithm to measure the object similarity. The numerical results indicate that both the accuracy and diversity could be improved greatly compared with the standard HC algorithm and the optimal values reached simultaneously. On the Movielens and Netflix datasets, the algorithmic accuracy, measured by the average ranking score, can be improved by 39.7% and 56.1% in the optimal case, respectively, and the diversity could reach 0.9587 and 0.9317 when the recommendation list equals to 5. Further statistical analysis indicates that, in the optimal case, the distributions of the edge weight are changed to the Poisson form, which may be the reason why HC algorithm performance could be improved. This work highlights the effect of edge weight on a personalized recommendation study, which maybe an important factor affecting personalized recommendation performance.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301833
Statistics

Document views: 23 File downloads:
  • zha_ifw.pdf: 50