Next Article in Journal
Do Cryptocurrency Prices Camouflage Latent Economic Effects? A Bayesian Hidden Markov Approach
Previous Article in Journal
Considered Factors of Online News Based on Respondents’ Eye Activity Using Eye-Tracker Analysis
Open AccessArticle

Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding

by Yifei Zhou 1, Shaoyong Li 1,* and Yaping Liu 2,*
School of Computer Science and Engineering, Central South University, Changsha 410083, China
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Authors to whom correspondence should be addressed.
Future Internet 2020, 12(3), 58;
Received: 12 February 2020 / Revised: 18 March 2020 / Accepted: 18 March 2020 / Published: 20 March 2020
(This article belongs to the Section Big Data and Augmented Intelligence)
Smartphones and applications have become widespread more and more. Thus, using the hardware and software of users’ mobile phones, we can get a large amount of personal data, in which a large part is about the user’s application usage patterns. By transforming and extracting these data, we can get user preferences, and provide personalized services and improve the experience for users. In a detailed way, studying application usage pattern benefits a variety of advantages such as precise bandwidth allocation, App launch acceleration, etc. However, the first thing to achieve the above advantages is to predict the next application accurately. In this paper, we propose AHNEAP, a novel network embedding based framework for predicting the next App to be used by characterizing the context information before one specific App being launched. AHNEAP transforms the historical App usage records in physical spaces to a large attributed heterogeneous network which contains three node types, three edges, and several attributes like App type, the day of the week. Then, the representation learning process is conducted. Finally, the App usage prediction problem was defined as a link prediction problem, realized by a simple neural network. Experiments on the LiveLab project dataset demonstrate the effectiveness of our framework which outperforms the three baseline methods for each tested user. View Full-Text
Keywords: app usage; prediction; attributed heterogeneous network; link prediction app usage; prediction; attributed heterogeneous network; link prediction
Show Figures

Figure 1

MDPI and ACS Style

Zhou, Y.; Li, S.; Liu, Y. Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding. Future Internet 2020, 12, 58.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop