With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.

中文翻译：

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基于多图神经网络的移动应用跨域推荐

随着移动应用生态系统的快速发展，移动应用变得越来越流行。应用程序的爆炸式增长使得用户很难找到符合他们兴趣的应用程序。因此，有必要为用户推荐一套个性化的应用程序。然而，挑战之一是数据稀疏，因为用户的历史行为数据通常不足。事实上，应用商店中不同域的用户对相同应用的行为通常是相关的。因此，我们可以使用来自相关域的互补信息来缓解稀疏性。使用图对用户的行为进行建模是直观的，图神经网络在表示学习方面表现出了强大的力量。在本文中，我们提出了一种新颖的模型，即深度多图嵌入 (DMGE)，用于学习跨域应用嵌入。具体来说，我们首先根据用户在不同领域的行为构建多图，然后提出多图神经网络来学习跨域应用嵌入。特别是，我们提出了一种自适应方法来平衡每个域的权重并有效地训练模型。最后，我们基于学习到的应用嵌入实现了跨域应用推荐。对现实世界数据集的广泛实验表明，DMGE 优于其他最先进的嵌入方法。