Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

中文翻译：

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App2Vec：上下文感知应用程序使用预测

应用程序开发人员和服务提供商都有强烈的动机去了解什么时候和在哪里某些应用程序被用户使用。然而，由于高度倾斜和嘈杂的应用程序使用数据，这一直是一个具有挑战性的问题。此外，现有研究将应用程序视为独立项目，未能捕捉到应用程序使用轨迹中隐藏的语义。在本文中，我们提出了 App2Vec，这是一种强大的表示学习模型，用于在考虑时空上下文的情况下学习应用程序的语义嵌入。基于获得的语义嵌入，我们开发了一个基于贝叶斯混合模型和狄利克雷过程的概率模型来捕获什么时候,在哪里， 和什么应用程序的语义用于预测未来的使用情况。我们使用两个不同的应用程序使用数据集评估我们的模型，这些数据集涉及超过 170 万用户和 2,000 多个应用程序。评估结果表明，我们提出的 App2Vec 算法在应用程序使用预测方面优于最先进的算法，性能差距超过 17.0%。