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Effects of high-order correlations on personalized recommendations for bipartite networks

Author

Listed:
  • Liu, Jian-Guo
  • Zhou, Tao
  • Che, Hong-An
  • Wang, Bing-Hong
  • Zhang, Yi-Cheng

Abstract

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user–user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence of mainstream preferences. Simulation results show that the algorithmic accuracy, measured by the average ranking score, is further improved by 20.45% and 33.25% in the optimal cases of MovieLens and Netflix data. More importantly, the optimal value λopt depends approximately monotonously on the sparsity of the training set. Given a real system, we could estimate the optimal parameter according to the data sparsity, which makes this algorithm easy to be applied. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that as the sparsity increases, the algorithm considering the second-order correlation can outperform the MCF simultaneously in all three criteria.

Suggested Citation

  • Liu, Jian-Guo & Zhou, Tao & Che, Hong-An & Wang, Bing-Hong & Zhang, Yi-Cheng, 2010. "Effects of high-order correlations on personalized recommendations for bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 881-886.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:4:p:881-886
    DOI: 10.1016/j.physa.2009.10.027
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    Citations

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    Cited by:

    1. Geng, Bingrui & Li, Lingling & Jiao, Licheng & Gong, Maoguo & Cai, Qing & Wu, Yue, 2015. "NNIA-RS: A multi-objective optimization based recommender system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 383-397.
    2. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Liu, Runran, 2016. "Targeted attack on networks coupled by connectivity and dependency links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 687-699.
    3. Pan, Ying & Li, De-Hua & Liu, Jian-Guo & Liang, Jing-Zhang, 2010. "Detecting community structure in complex networks via node similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2849-2857.
    4. Shi, Xiaoyu & Shang, Ming-Sheng & Luo, Xin & Khushnood, Abbas & Li, Jian, 2017. "Long-term effects of user preference-oriented recommendation method on the evolution of online system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 490-498.
    5. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.
    6. 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.
    7. Zhang, Zi-Ke & Yu, Lu & Fang, Kuan & You, Zhi-Qiang & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 82-88.

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