Affiliations: [a] College of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, China
| [b]
Lenovo Machine Intelligence Center, Lenovo Group Limited, HongKong, China
Correspondence:
[*]
Corresponding author. Chao Li, College of Computer Science and Engineering, Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, 266510, Qingdao, China. E-mail: [email protected].
Abstract: Owing the continuous enrichment of mobile application resources, mobile applications carry almost all user behaviors and preferences. The analysis of user behavior regarding mobile terminals has become an important research direction. The frequency with which users click on mobile applications reflects their preferences to a certain extent. In this study, we propose a mobile application click-frequency prediction model based on heterogeneous information network representation. This model first constructs a heterogeneous information network between users’ mobile devices and mobile applications. To generate a meaningful sequence of network-embedded nodes, we perform a random walk on a specified meta-path. Finally, the prediction of users’ mobile application click frequency is completed using representation fusion and matrix factorization. Experiments show that our method outperforms other baseline methods in terms of the mean absolute error and root mean square error. Therefore, the application of a heterogeneous information network representation method to the prediction model is effective. This study is significant to the behavior research of mobile terminal users.
Keywords: Heterogeneous information network, network representation learning, prediction algorithm, mobile application
