Sequence-Graph Fusion Neural Network for User Mobile App Behavior Prediction

Wang, Yizhuo1; Jiang, Renhe2; Liu, Hangchen1; Yin, Du1; Song, Xuan1

10.1007/978-3-031-43427-3_7

2023

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

2945-9133

1611-3349

978-3-031-43426-6

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: APPLIED DATA SCIENCE AND DEMO TRACK, ECML PKDD 2023, PT VI

14174

SEP 18-22, 2023

null,Turin,ITALY

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

In recent years, mobile applications (apps) on smartphones have shown explosive growth. Massive and diversified apps greatly affect user experience. As a result, user mobile app behavior prediction has become increasingly important. Existed algorithms based on deep learning mainly conduct sequence modeling on the app usage historical records, which are insufficient in capturing the similarity between users and apps, and ignore the semantic associations in app usage. Although some works have tried to model from the perspective of graph structure recently, the two types of modeling methods have not been combined, and whether they are complementary has not been explored. Therefore, we propose an SGFNN model based on sequence combined graph modeling, which is already publicly available as the GitHub repository https://github.com/ZAY113/SGFNN. Sequence Block, BipGraph Block, and HyperGraph Block are used to capture the user mobile app behavior short-term pattern, the similarity between users and apps, and the semantic relations of hyperedge "user-time-location-app", respectively. Two real-world datasets are selected in our experiments. When the app sequence length is 4, the prediction accuracy of Top1, Top5, and Top10 reaches 36.08%, 68.39%, 79.02% and 51.55%, 87.57%, 95.62%, respectively. The experimental results show that the two modeling methods can be combined to improve prediction accuracy, and the information extracted from them is complementary.

User app behavior prediction Deep learning Graph neural networks Hypergraph embedding

第一 ; 通讯

英语

EI ; CPCI-S

National Key Research and Development Project of China[2021YFB1714400]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:001156143700007

Web of Science

1.Southern University of Science and Technology, Shenzhen, China
2.The University of Tokyo, Tokyo, Japan

Wang, Yizhuo,Jiang, Renhe,Liu, Hangchen,et al. Sequence-Graph Fusion Neural Network for User Mobile App Behavior Prediction[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2023.