论文标题
基于知识图的移动应用程序建议
A Knowledge Graph based Approach for Mobile Application Recommendation
论文作者
论文摘要
随着移动设备的迅速流行和移动应用程序的急剧扩散（APP），应用推荐成为一项紧急任务，将使应用程序用户和股东受益。如何有效地组织和充分利用用户和应用程序的丰富信息是解决传统方法的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），以进行应用建议。具体而言，我们首先设计了一种知识图构造方法来对用户和应用程序侧面信息进行建模，然后采用了KG嵌入技术来捕获与KG的一阶结构相关的侧面信息的事实，并最终提出了一种关系互惠卷积的卷积嵌入嵌入繁殖模型，以捕获与高级相关的kg kg kg of kg kg of kg of kg of kg sy kg的繁殖。与最先进的建议方法相比，在现实世界数据集上进行的广泛实验验证了所提出的方法的有效性。
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.
