Smartphone 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%.

中文翻译：

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深度应用

智能手机移动应用（App）使用预测，即接下来会使用哪些应用，有利于用户体验的提升。通过对现实世界数据集的深入分析，我们发现应用程序的使用具有高度时空相关性和个性化。鉴于能够对复杂的时空上下文进行建模，我们的目标是应用深度学习来实现高预测精度。然而，个性化产生了一个问题：为每个人训练一个网络会受到数据稀缺的困扰，而为所有用户训练一个深度神经网络往往无法揭示用户偏好。在本文中，我们提出了一个新颖的应用使用预测框架，命名为深度应用，通过多任务学习实现上下文感知预测。为了应对数据稀缺的挑战，我们为多个用户训练一个通用网络以共享共同模式。为了更好地利用时空上下文，我们在多任务学习框架中补充了一个位置预测任务来学习时空关系。至于个性化，我们添加了一个用户识别任务来捕获用户偏好。我们通过大量实验在大规模数据集上评估 DeepApp。结果表明，DeepApp 的性能优于最先进的基线 6.44%。