Abstract
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.
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Acknowledgements
This work was partially supported by the National Key R&D Program of China (Grant No. 2017YFB1001801), the National Natural Science Foundation of China (Grant Nos. 61672278, 61373128, 61321491), the science and technology project from State Grid Corporation of China (Contract No. SGSNXT00YJJS1800031), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Sino-German Institutes of Social Computing.
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Xu, S. et al. (2018). Predicting Smartphone App Usage with Recurrent Neural Networks. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_44
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DOI: https://doi.org/10.1007/978-3-319-94268-1_44
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