Abstract
The popularity of smartphones has witnessed the rapid growth of the number of mobile applications. Nowadays, there are millions of applications available, and at the same time, many applications are already installed on people’s smartphones. Installing numerous apps will cause some troubles in finding the specific apps promptly. Hence it is necessary to predict the next app(s) to be used in a short term and launching them as shortcuts, which will make the smartphone system more efficient and user-friendly. In this paper, we pay attention to two subproblems that are related to the app usage prediction. One is the \(\varDelta T\) app prediction problem that focuses on predicting a set of apps that will be used in a time interval. The other is the Top-K app recommendation problem that focuses on recommending the K most probable APPs to be used next. In order to solve these problems, we propose a generic prediction model based on Long Short-term Memory (LSTM), which is an enhancement of the recurrent neural network (RNN) model. The proposed model converts the temporal-sequence dependency and contextual information into a unified feature representation for next app prediction. We implement the model in the Android platform. Extensive experiments based on real collected dataset demonstrate that the proposed LSTM model outperforms the baselines for app usage prediction, and achieves high accuracy for app recommendation.
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Acknowledgements
This work was partially supported by the National Key R&D Program of China (Grant No. 2018YFB1004704), the National Natural Science Foundation of China (Grant Nos. 61972196, 61672278, 61832008, 61832005), the Key R&D Program of Jiangsu Province, China (Grant No. BE2018116), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Sino-German Institutes of Social Computing.
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Xu, S., Li, W., Zhang, X. et al. Predicting and Recommending the next Smartphone Apps based on Recurrent Neural Network. CCF Trans. Pervasive Comp. Interact. 2, 314–328 (2020). https://doi.org/10.1007/s42486-020-00045-z
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DOI: https://doi.org/10.1007/s42486-020-00045-z