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Personalized recommendation via unbalance full-connectivity inference

Author

Listed:
  • Ma, Wenping
  • Ren, Chen
  • Wu, Yue
  • Wang, Shanfeng
  • Feng, Xiang

Abstract

Recommender systems play an important role to help us to find useful information. They are widely used by most e-commerce web sites to push the potential items to individual user according to purchase history. Network-based recommendation algorithms are popular and effective in recommendation, which use two types of elements to represent users and items respectively. In this paper, based on consistence-based inference (CBI) algorithm, we propose a novel network-based algorithm, in which users and items are recognized with no difference. The proposed algorithm also uses information diffusion to find the relationship between users and items. Different from traditional network-based recommendation algorithms, information diffusion initializes from users and items, respectively. Experiments show that the proposed algorithm is effective compared with traditional network-based recommendation algorithms.

Suggested Citation

  • Ma, Wenping & Ren, Chen & Wu, Yue & Wang, Shanfeng & Feng, Xiang, 2017. "Personalized recommendation via unbalance full-connectivity inference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 273-279.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:273-279
    DOI: 10.1016/j.physa.2017.04.041
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    References listed on IDEAS

    as
    1. Yu, Fei & Zeng, An & Gillard, Sébastien & Medo, Matúš, 2016. "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 192-208.
    2. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.
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    Citations

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    Cited by:

    1. S. Bhaskaran & Raja Marappan & B. Santhi, 2020. "Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets," Mathematics, MDPI, vol. 8(7), pages 1-27, July.

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