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Collaborative filtering based on multi-channel diffusion

Author

Listed:
  • Shang, Ming-Sheng
  • Jin, Ci-Hang
  • Zhou, Tao
  • Zhang, Yi-Cheng

Abstract

In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain rating and a user having voted an object will be connected to the channel corresponding to the rating. Diffusion process taking place on such a user–channel bipartite graph gives a new similarity measure of user pairs, which is further demonstrated to be more accurate than the classical Pearson correlation coefficient under the standard collaborative filtering framework.

Suggested Citation

  • Shang, Ming-Sheng & Jin, Ci-Hang & Zhou, Tao & Zhang, Yi-Cheng, 2009. "Collaborative filtering based on multi-channel diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4867-4871.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:23:p:4867-4871
    DOI: 10.1016/j.physa.2009.08.011
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    Citations

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    Cited by:

    1. Zhu, Xuzhen & Tian, Hui & Zhang, Tianqiao, 2018. "Symmetrical information filtering via punishing superfluous diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 1-9.
    2. Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
    3. Yin, Chun-Xia & Peng, Qin-Ke & Chu, Tao, 2012. "Personal artist recommendation via a listening and trust preference network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 1991-1999.
    4. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.

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