Predicting Smartphone App Usage with Recurrent Neural Networks
| Autor: | Wenzhong Li, Tianzi Sun, Xiao Zhang, Shijian Xu, Tong Zhan, Yongzhu Zhao, Wei-wei Zhu, Songcheng Gao |
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| Rok vydání: | 2018 |
| Předmět: |
Dependency (UML)
business.industry Computer science 02 engineering and technology Machine learning computer.software_genre Term (time) Recurrent neural network 020204 information systems Smartphone app 0202 electrical engineering electronic engineering information engineering Contextual information 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Wireless Algorithms, Systems, and Applications ISBN: 9783319942674 WASA |
| DOI: | 10.1007/978-3-319-94268-1_44 |
| Popis: | Nowadays millions of apps are available and most of users install a lot of apps on their smartphones. It will cause some troubles in finding the specific apps promptly. By predicting the next app to be used in a short term and launching them as shortcuts can make the smartphone system more efficient and user-friendly. In this paper, we formulate the app usage prediction problem as a multi-label classification problem and propose a prediction model based on Long Short-term Memory (LSTM), which is an extension of the recurrent neural network (RNN). The proposed model explores the temporal-sequence dependency and contextual information as features for prediction. Extensive experiments based on real collected dataset show that the proposed model achieves better performance compared to the conventional approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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