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Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding

Author

Listed:
  • Shaoyong Li

    (College of Mathematics and Computer Science, Changsha University, Changsha 410083, China
    College of Computer Science, National University of Defense Technology, Changsha 410073, China)

  • Liang Lv

    (School of Computer Science and Engineering, Tsinghua University, Beijing 410083, China)

  • Xiaoya Li

    (College of Mathematics and Computer Science, Changsha University, Changsha 410083, China)

  • Zhaoyun Ding

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. There are some existing works related to mobile App start-up prediction using multi-user data, which require the integration of multi-party data. In this case, a typical solution is distributed learning of centralized computing. However, this solution can easily lead to the leakage of user privacy data. In this paper, we propose a mobile App start-up prediction method based on federated learning and attributed heterogeneous network embedding, which alleviates the cold start problem of new users or new Apps while guaranteeing users’ privacy.

Suggested Citation

  • Shaoyong Li & Liang Lv & Xiaoya Li & Zhaoyun Ding, 2021. "Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding," Future Internet, MDPI, vol. 13(10), pages 1-20, October.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:10:p:256-:d:651427
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    Cited by:

    1. Juntao Li & Tianxu Cui & Kaiwen Yang & Ruiping Yuan & Liyan He & Mengtao Li, 2021. "Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 13(23), pages 1-29, November.

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