Details

Autor(en) / Beteiligte

• Xia, Tong
• Li, Yong
• Feng, Jie
• Jin, Depeng
• Zhang, Qing
• Luo, Hengliang
• Liao, Qingmin

Titel

DeepApp: Predicting Personalized Smartphone App Usage via Context-Aware Multi-Task Learning

Ist Teil von

• ACM transactions on intelligent systems and technology, 2020-11, Vol.11 (6), p.1-12

Erscheinungsjahr

2020

Quelle

ACM Digital Library

Beschreibungen/Notizen

• Smartphone mobile application (App) usage prediction, i.e., which Apps will be used next, is beneficial for user experience improvement. Through an in-depth analysis on a real-world dataset, we find that App usage is highly spatio-temporally correlated and personalized. Given the ability to model complex spatio-temporal contexts, we aim to apply deep learning to achieve high prediction accuracy. However, the personalization yields a problem: training one network for each individual suffers from data scarcity, yet training one deep neural network for all users often fails to uncover user preference. In this article, we propose a novel App usage prediction framework, named DeepApp , to achieve context-aware prediction via multi-task learning. To tackle the challenge of data scarcity, we train one general network for multiple users to share common patterns. To better utilize the spatio-temporal contexts, we supplement a location prediction task in the multi-task learning framework to learn spatio-temporal relations. As for the personalization, we add a user identification task to capture user preference. We evaluate DeepApp on the large-scale dataset by extensive experiments. Results demonstrate that DeepApp outperforms the start-of-the-art baseline by 6.44%.