一种基于双向注意力的知识图谱神经网络推荐-国际计算机科学进展-CSCIED科技核心评价数据库-手机版

一种基于双向注意力的知识图谱神经网络推荐

A neural network recommender algorithm based on bidirectional graph attention

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DOI	10.12208/j.aics.20220057
刊名	国际计算机科学进展 Advances in International Computer Science
年，卷(期)	2022, 2(4)
作者	宁辉*,王荣胜,杨鹏伟,
作者单位	河南理工大学河南焦作 ; 澳门理工大学中国澳门 ; 广州大学广东广州 ;
摘要	在推荐系统中，基于知识图谱的神经网络推荐以图作为输入，可以很好地将节点信息和拓扑结构相结合进行预测。然而现有方法中，很少考虑图结构中存在的对称关系以及信息聚合时梯度消失的问题。本文提出一种双向注意力机制的知识图谱神经网络推荐算法，首先将图神经网络与对称注意力机制相结合，然后采用双向翻译模型对知识图谱中用户-项目信息进行特征的嵌入表示，使得注意力机制在决策权重时考虑的关系更全面。其次，在对节点和邻居信息训练过程中，为避免过拟合问题引入了多通道激活函数。最后，在两个真实数据集上与经典算法进行对比，验证了本文所提出算法的有效性。
Abstract	Comparing with the traditional neural network, the neural network based on knowledge graph (KG) takes the graph as the input in the recommender system, which can combine the node information and topology for prediction. However, the existing methods rarely consider the symmetry relationship in KG and the disappearance of the gradient. A Neural Network Recommender Algorithm Based on Bidirectional Graph Attention (BGANR) is proposed. Firstly, the graph neural network with the symmetrical attention mechanism and the bidirectional translation model are combined to embed the KG information. Then, the dynamic activation function is used to avoid increasing of calculation and overfitting. Empirical results on two benchmark datasets demonstrate our model outperforms state-of-the-art methods.
关键词	双向嵌入；注意力机制；知识图谱；推荐
KeyWord	Bidirectional embedding; Attention mechanism; Knowledge graph; Recommender
基金项目
页码	6-10

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引用本文

宁辉*,王荣胜,杨鹏伟. 一种基于双向注意力的知识图谱神经网络推荐 [J]. 国际计算机科学进展. 2022; 2; (4). 6 - 10.

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