With the rise of the mobile Internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. However, most of the existing recommended methods for apps ignore the app functional exclusiveness features, which makes it difficult to further improve the app recommendation performance. To solve this problem, we propose a personalized context-aware mobile app recommendation approach, called PCMARA. Specifically, (1) PCMARA explores the relationship between contextual information and function of apps and constructs the app contextual factors for app which represent the function of app. (2) PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate the adverse effects that ignore the app functional exclusiveness. (3) PCMARA comprehensively considers the contextual information of users and apps to generate a recommendation list for users based on the target users’ current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results demonstrate the superiority of PMARA over the benchmark methods.

随着移动互联网的兴起，移动应用（app）的数量呈现爆发式增长，这直接导致了应用数据过载。目前，推荐系统已成为解决应用数据过载最有效的方法。然而，现有的应用推荐方法大多忽略了应用功能独占性特征，难以进一步提升应用推荐性能。为了解决这个问题，我们提出了一种个性化的上下文感知移动应用推荐方法，称为 PCMARA。具体而言，(1)PCMARA 探索了应用上下文信息与应用功能之间的关系，构建了代表应用功能的应用上下文因素。(2) PCMARA 利用应用上下文因素设计新颖的应用相似性模型，从而有效消除忽视应用功能排他性的不良影响。(3)PCMARA综合考虑用户和app的上下文信息，根据目标用户当前的时间和位置为用户生成推荐列表。我们将 PCMARA 应用于真实世界的数据集，并进行了大规模的推荐效果实验。实验结果证明了 PMRA 优于基准方法。