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Trend Prediction In Temporal Bipartite Networks: The Case Of Movielens, Netflix, And Digg

Author

Listed:
  • AN ZENG

    (Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland)

  • STANISLAO GUALDI

    (Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland)

  • MATÚŠ MEDO

    (Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland)

  • YI-CHENG ZHANG

    (Physics Department, University of Fribourg, CH-1700 Fribourg, Switzerland)

Abstract

Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.

Suggested Citation

  • An Zeng & Stanislao Gualdi & Matúš Medo & Yi-Cheng Zhang, 2013. "Trend Prediction In Temporal Bipartite Networks: The Case Of Movielens, Netflix, And Digg," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-15.
    • Handle: RePEc:wsi:acsxxx:v:16:y:2013:i:04n05:n:s0219525913500240
    DOI: 10.1142/S0219525913500240
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    Citations

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    Cited by:

    1. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Ming-Yang Zhou & Yi-Cheng Zhang, 2018. "Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    2. Wu, Jiayun & He, Langzhou & Jia, Tao & Tao, Li, 2023. "Temporal link prediction based on node dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    3. Liebig, Jessica & Rao, Asha, 2016. "Predicting item popularity: Analysing local clustering behaviour of users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 523-531.
    4. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Mingyang Zhou & Yi-Cheng Zhang, 2019. "Enhancing countries' fitness with recommender systems on the international trade network," Papers 1904.02412, arXiv.org.
    5. Li, Wen-Jun & Dong, Qiang & Shi, Yang-Bo & Fu, Yan & He, Jia-Lin, 2017. "Effect of recent popularity on heat-conduction based recommendation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 334-343.
    6. 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.
    7. Wang, Xi & Li, Heyang & Zeng, An, 2018. "Quantifying users’ selection behavior in online commercial systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 86-95.
    8. Li, Heyang & Zeng, An, 2022. "Improving recommendation by connecting user behavior in temporal and topological dimensions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    9. Zhou, Yanbo & Li, Qu & Yang, Xuhua & Cheng, Hongbing, 2021. "Predicting the popularity of scientific publications by an age-based diffusion model," Journal of Informetrics, Elsevier, vol. 15(4).

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