The emergence of a large number of mobile apps brings challenges to locate appropriate apps for users, which makes mobile app recommendation an imperative task. In this paper, we first conduct detailed data analysis to show the characteristics of mobile apps which are different with conventional items (e.g., movies, books). Considering the specific property of mobile apps, we propose a broad learning approach for context-aware mobile app recommendation with tensor analysis (CAMAR). Specifically, we utilize a tensor-based framework to effectively integrate app category information and multi-view features on users and apps to facilitate the performance of app recommendation. The multi-dimensional structure is employed to capture the hidden relationships among the app categories and multi-view features. We develop an efficient factorization method which applies Tucker decomposition to jointly learn the full-order interactions among the app categories and features without physically building the tensor. Furthermore, we employ a group \(\ell _{1}\)-norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

中文翻译：

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CAMAR：针对移动应用程序的广泛的基于学习的上下文感知推荐器

大量移动应用的出现给定位适合用户的应用带来了挑战，这使得移动应用推荐成为当务之急。在本文中，我们首先进行详细的数据分析，以显示与常规项目（例如电影，书籍）不同的移动应用程序的特征。考虑到移动应用程序的特定属性，我们针对具有张量分析（CAMAR）的上下文感知移动应用程序推荐提出了一种广泛的学习方法。具体来说，我们利用基于张量的框架来有效地集成用户和应用上的应用类别信息和多视图功能，以促进应用推荐的性能。多维结构用于捕获应用程序类别和多视图功能之间的隐藏关系。我们开发了一种有效的因式分解方法，该方法应用Tucker分解来共同学习应用类别和功能之间的全序交互，而无需物理构建张量。此外，我们雇用了一个小组\（\ ell _ {1} \）-规范化正则化，以了解每个视图相对于每个应用类别的按组分类的重要性。在两个真实世界的数据集上进行的实验证明了该方法的有效性。