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DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction
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DOI：10.1109/TMC.2021.3093619.png)
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摘要
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This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and predicts a set of apps at one time. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6 percent and recall of 62.4 percent) and reduces the prediction time of the state-of-the-art by 6.58x. A field experiment of 29 participants demonstrates DeepAPP can effectively reduce launch time of apps.
Keyword:
Reinforcement learning
Predictive models
Mobile computing
Neural networks
Poles and towers
Servers
Games
Mobile devices
app usage prediction
deep reinforcement learning
neural networks
期刊
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9.2
论文数:
5.8K
被引数:
1.8W
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