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Using combined network information to predict mobile application usage

Author

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  • Jiang, Yubo
  • Du, Xin
  • Jin, Tao

Abstract

Recently, mobile applications are widely used by smartphone owners. The understanding of application usage can help us make prediction on its development tendency and meanwhile improve users’ experience. To predict the future application usage, we develop a simple but novel method that considers two types of user-related networks (the users’ call-log based network and the newly developed “application usage similarity network”) as well as the correlations between user characteristics and application functions. Our model is tested with data from 25,376 users selected from the largest connected component of total users in a local operator in China, and results show that the model with the combined network achieves the best result (almost 60% prediction precision with 60% recall) compared with models on a single related network (the call-log based network or the application usage similarity network) or without network information.

Suggested Citation

  • Jiang, Yubo & Du, Xin & Jin, Tao, 2019. "Using combined network information to predict mobile application usage," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 430-439.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:430-439
    DOI: 10.1016/j.physa.2018.09.135
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    References listed on IDEAS

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    1. Ren, Fei & Li, Sai-Ping & Liu, Chuang, 2017. "Information spreading on mobile communication networks: A new model that incorporates human behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 334-341.
    2. Zhang, Aihua & Zheng, Mingxing & Pang, Bowen, 2018. "Structural diversity effect on hashtag adoption in Twitter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 267-275.
    3. Alnawas, Ibrahim & Aburub, Faisal, 2016. "The effect of benefits generated from interacting with branded mobile apps on consumer satisfaction and purchase intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 313-322.
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    1. Jiang, Yubo & Zhu, Yunfang & Du, Xin & Jin, Tao, 2019. "The implicit network inferred from users’ residences and workplaces enhancing collaborative recommendation on smartphones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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