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Information filtering via collaborative user clustering modeling

Author

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  • Zhang, Chu-Xu
  • Zhang, Zi-Ke
  • Yu, Lu
  • Liu, Chuang
  • Liu, Hao
  • Yan, Xiao-Yong

Abstract

The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information but also the user information. In addition, we compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on two real-world datasets, MovieLens 1M and MovieLens 100k, show that our method performs better than other three methods in the accuracy of recommendation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:396:y:2014:i:c:p:195-203
    DOI: 10.1016/j.physa.2013.11.024
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    References listed on IDEAS

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    1. Shang, Ming-Sheng & Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Collaborative filtering with diffusion-based similarity on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1259-1264.
    2. Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 179-186.
    3. Tian Qiu & Zi-Ke Zhang & Guang Chen, 2013. "Information Filtering via a Scaling-Based Function," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
    4. Zhang, Chu-Xu & Zhang, Zi-Ke & Liu, Chuang, 2013. "An evolving model of online bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6100-6106.
    5. Jianguo Liu & Lei Hou & Yi-Lu Zhang & Wen-Jun Song & Xue Pan, 2013. "Empirical Analysis Of The Clustering Coefficient In The User-Object Bipartite Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(08), pages 1-10.
    6. Qiang Guo & Rui Leng & Kerui Shi & Jian-Guo Liu, 2012. "Information Filtering Via Clustering Coefficients Of User–Object Bipartite Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-14.
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    Cited by:

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    2. 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.
    3. Ma, Tinghuai & Suo, Xiafei & Zhou, Jinjuan & Tang, Meili & Guan, Donghai & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2016. "Augmenting matrix factorization technique with the combination of tags and genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 101-116.
    4. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.
    5. Qian, Fulan & Zhao, Shu & Tang, Jie & Zhang, Yanping, 2016. "SoRS: Social recommendation using global rating reputation and local rating similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 61-72.

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