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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Attentive Meta-graph Embedding for item Recommendation in heterogeneous information networks
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Attentive Meta-graph Embedding for item Recommendation in heterogeneous information networks

机译：在异构信息网络中嵌入物品建议的细心元图

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摘要

Heterogeneous information network (HIN) has become increasingly popular to be exploited in recommender systems, since it contains abundant semantic information to help generate better recommendations. Most conventional work employs meta-paths to model the rich semantics in the HIN. However, the meta-path as a linear structure is insufficient to express the connections. Recently, several work adopts a graph structure, i.e. meta-graph, to express the complex semantics. However, they treat the contributions of nodes in the meta-graph equally, and no explicit representations for users, items or meta-graph based context are learned in the process. To tackle the above problems, this paper proposes an Attentive Meta-graph Embedding approach for item Recommendation, called AMERec, in HINs. Firstly, we prioritize those highly similar pairwise features in the selection of meta-graph instances. Secondly, we differentiate each node in the meta-graph and learn an embedding for each meta-graph. Thirdly, we consider the differences between user and item pairs based on their meta-graph context, and learn a weight for each meta-graph by leveraging the attention mechanism. Finally, we predict the rating by capturing the low- and high-dimensional interaction information between users, items and their meta-graph based context. Comprehensive experiments on three different datasets show that the proposed method is superior to other comparative methods. (C) 2020 Elsevier B.V. All rights reserved.
机译：异构信息网络（HIN）包含丰富的语义信息，有助于生成更好的推荐，因此在推荐系统中得到越来越广泛的应用。大多数传统的工作都使用元路径来建模HIN中丰富的语义。然而，作为线性结构的元路径不足以表示连接。最近，有几项工作采用了一种图结构，即元图来表达复杂的语义。然而，它们平等地对待元图中节点的贡献，并且在这个过程中没有学习到用户、项目或基于元图的上下文的显式表示。为了解决上述问题，本文提出了一种在HINs中嵌入元图的项目推荐方法AMERec。首先，我们在选择元图实例时优先考虑那些高度相似的成对特征。其次，我们区分元图中的每个节点，并学习每个元图的嵌入。第三，我们考虑基于元图上下文的用户和项目对之间的差异，并利用注意机制学习每个元图的权重。最后，我们通过捕捉用户、项目及其基于元图的上下文之间的低维和高维交互信息来预测评分。在三个不同数据集上的综合实验表明，该方法优于其他比较方法。（C） 2020爱思唯尔B.V.版权所有。

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