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

Shaoyong Li, Liang Lv, Xiaoya Li and Zhaoyun Ding
Additional contact information
Shaoyong Li: College of Mathematics and Computer Science, Changsha University, Changsha 410083, 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

Future Internet, 2021, vol. 13, issue 10, 1-20

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.

Keywords: mobile app start-up prediction; federated learning; app usage; prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/10/256/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/10/256/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:10:p:256-:d:651427

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().