One Improved Collaborative Filtering Method Based on Information Transformation


  •  Zhaoxing Liu    
  •  Ning Zhang    

Abstract

In this paper, we propose a novel method combined classical collaborative filtering (CF) and bipartite network structure. Different from the classical CF, user similarity is viewed as personal recommendation power and during the recommendation process; it will be redistributed to different users. Furthermore, a free parameter is introduced to tune the contribution of the user to the user similarity. Numerical results demonstrate that decreasing the degree of user to some extent in method performs well in rank value and hamming distance. Furthermore, the correlation between degree and similarity is concerned to solved the drastically change of our method performance.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-8989
  • ISSN(Online): 1913-8997
  • Started: 2008
  • Frequency: quarterly

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WJCI (2020): 0.439

Impact Factor 2020 (by WJCI): 0.247

Google Scholar Citations (March 2022): 6907

Google-based Impact Factor (2021): 0.68

h-index (December 2021): 37

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