Mobile App Recommendation with Sequential App Usage Behavior Tracking

Yongkeun Hwang,
Donghyeon Lee,
Kyomin Jung,

Abstract


The recent evolution of mobile devices and services have resulted in such plethora of mobile applications (apps) that users have difficulty finding the ones they wish to use in a given moment. We design an app recommendation system which predicts the app to be executed with high accuracy so that users are able to access their next app conveniently and quickly. We introduce the App-Usage Tracking Feature (ATF), a simple but powerful feature for predicting next app launches, which characterizes each app use from the sequence of previously used apps. In addition, our method can be implemented without compromising the user privacy since it is solely trained on the target user’s mobile usage data and it can be conveniently implemented in the individual mobile device because of its less computation-intensive behavior. We provide a comprehensive empirical analysis of the performance and characteristics of our proposed method on real-world mobile usage data. We also demonstrate that our system can accurately predict the next app launches and outperforms the baseline methods such as the most frequently used apps (MFU) and the most recently used apps (MRU).


Citation Format:
Yongkeun Hwang, Donghyeon Lee, Kyomin Jung, "Mobile App Recommendation with Sequential App Usage Behavior Tracking," Journal of Internet Technology, vol. 20, no. 3 , pp. 827-837, May. 2019.

Full Text:

PDF

Refbacks

  • There are currently no refbacks.





Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Library and Information Center, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd. Shoufeng, Hualien 97401, Taiwan, R.O.C.
Tel: +886-3-931-7017  E-mail: jit.editorial@gmail.com