Name
Title | ||
|---|---|---|
DeepApp: Predicting Personalized Smartphone App Usage via Context-Aware Multi-Task Learning |
Abstract | ||
|---|---|---|
AbstractSmartphone 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%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3408325 | ACM Transactions on Intelligent Systems and Technology |
Keywords | DocType | Volume |
App usage prediction, multi-task learning, deep learning | Journal | 11 |
Issue | ISSN | Citations |
6 | 2157-6904 | 1 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tong Xia | 1 | 1 | 0.34 |
| Yong Li | 2 | 2972 | 218.82 |
| Jie Feng | 3 | 82 | 7.13 |
| Depeng Jin | 4 | 2177 | 154.29 |
| Qing Zhang | 5 | 7 | 2.12 |
| Hengliang Luo | 6 | 9 | 3.50 |
| QM | 7 | 464 | 72.05 |