With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendation-focused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.

中文翻译：

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一种基于知识图谱的移动应用推荐方法

随着移动设备的迅速普及和移动应用程序 (app) 的急剧增加，应用程序推荐成为一项对应用程序用户和股东都有利的紧急任务。如何有效地组织和充分利用用户和应用丰富的边信息是解决传统方法稀疏问题的关键挑战。为了应对这一挑战，我们提出了一种新颖的端到端知识图卷积嵌入传播模型 (KGEP) 用于应用推荐。具体来说，我们首先设计了一种知识图谱构建方法来对用户和应用程序侧信息进行建模，然后采用 KG 嵌入技术来捕获与 KG 的一阶结构相关的侧信息的事实三重聚焦语义，最后提出了一个关系加权的卷积嵌入传播模型来捕获与 KG 的高阶结构相关的以推荐为重点的语义。与最先进的推荐方法相比，在真实世界数据集上进行的大量实验验证了所提出方法的有效性。