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Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs

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  • Zhang, Zi-Ke
  • Zhou, Tao
  • Zhang, Yi-Cheng

Abstract

Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:1:p:179-186
    DOI: 10.1016/j.physa.2009.08.036
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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. Zhang, Yin & Zhang, Bin & Gao, Kening & Guo, Pengwei & Sun, Daming, 2012. "Combining content and relation analysis for recommendation in social tagging systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5759-5768.
    3. Li, Jianguo & Tang, Yong & Chen, Jiemin, 2017. "Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 398-411.
    4. Yeh, Duen-Yian & Cheng, Ching-Hsue, 2015. "Recommendation system for popular tourist attractions in Taiwan using Delphi panel and repertory grid techniques," Tourism Management, Elsevier, vol. 46(C), pages 164-176.
    5. Shams, Bita & Haratizadeh, Saman, 2016. "SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 364-377.
    6. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    7. Zhou, Bin & He, Zhe & Wang, Nianxin & Xi, Zhendong & Li, Yujian & Wang, Bing-Hong, 2015. "On the optimization of multitasking process with multiplayer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 41-45.
    8. An, Ya-Hui & Dong, Qiang & Sun, Chong-Jing & Nie, Da-Cheng & Fu, Yan, 2016. "Diffusion-like recommendation with enhanced similarity of objects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 708-715.
    9. Wen, Yuan & Liu, Yun & Zhang, Zhen-Jiang & Xiong, Fei & Cao, Wei, 2014. "Compare two community-based personalized information recommendation algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 199-209.
    10. 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.
    11. 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.
    12. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    13. Wang, Jun & Zhang, Qian-Ming & Zhou, Tao, 2019. "Tag-aware link prediction algorithm in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 105-111.
    14. Zhang, Shujuan & Jin, Zhen & Zhang, Juan, 2016. "The dynamical modeling and simulation analysis of the recommendation on the user–movie network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 310-319.
    15. 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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