使用图负采样的图卷积神经网络推荐算法

黄河源; 慕彩红; 方云飞; 刘逸

doi:10.19665/j.issn1001-2400.20230214

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使用图负采样的图卷积神经网络推荐算法

计算机科学与技术 | 更新时间：2024-04-03

- 使用图负采样的图卷积神经网络推荐算法
- Graph convolution neural network for recommendation using graph negative sampling
- 西安电子科技大学学报 2024年51卷第1期页码：86-99
- 作者机构：
  
  1. 西安电子科技大学人工智能学院,陕西西安 710071
  2. 西安电子科技大学电子工程学院,陕西西安 710071
- 作者简介：
  
  [ "黄河源(1998—),男,西安电子科技大学硕士研究生,E-mail:[email protected];" ]
  慕彩红(1978—),女,教授,E-mail:[email protected]
  [ "方云飞(2000—),男,西安电子科技大学硕士研究生,E-mail:[email protected]; " ]
  [ "刘逸(1976—), 男, 讲师, E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(62077038);国家自然科学基金(61672405);国家自然科学基金(62176196);国家自然科学基金(62271374)
- DOI：10.19665/j.issn1001-2400.20230214
  中图分类号： TP181
- 收稿日期：2022-12-13，
  
  网络出版日期：2023-09-14，
  
  纸质出版日期：2024-01-20
- 稿件说明：
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黄河源, 慕彩红, 方云飞, 等. 使用图负采样的图卷积神经网络推荐算法[J]. 西安电子科技大学学报, 2024,51(1):86-99.

Heyuan HUANG, Caihong MU, Yunfei FANG, et al. Graph convolution neural network for recommendation using graph negative sampling[J]. Journal of xidian university, 2024, 51(1): 86-99.
黄河源, 慕彩红, 方云飞, 等. 使用图负采样的图卷积神经网络推荐算法[J]. 西安电子科技大学学报, 2024,51(1):86-99. DOI： 10.19665/j.issn1001-2400.20230214.

Heyuan HUANG, Caihong MU, Yunfei FANG, et al. Graph convolution neural network for recommendation using graph negative sampling[J]. Journal of xidian university, 2024, 51(1): 86-99. DOI： 10.19665/j.issn1001-2400.20230214.

摘要

经过几年的快速发展

基于图卷积神经网络的协同过滤算法已经在许多推荐系统场景中取得了最好的表现。但是这些算法在采集负样本时大多仅仅采用简单的随机负采样方法

没有充分利用图结构信息。针对这一问题

提出了一种使用图负采样的图卷积神经网络推荐算法GCN-GNS。该算法首先构造用户-物品二部图

并利用图卷积神经网络获取节点嵌入向量;接下来通过基于深度优先搜索的随机游走方法获取同时包含近距离邻居物品节点和远方物品节点的游走物品节点序列;然后设计注意力层自适应学习游走序列中不同节点的权重

并按权重组成一个动态更新的虚拟负样本;最终利用该虚拟负样本对模型进行更高效的训练。实验结果显示

与对比算法相比

多数情况下GCN-GNS在三个真实公开数据集上都有更好的表现;这表明所提出的新的图负采样方法能够帮助GCN-GNS算法更充分地利用图结构信息

并最终提升物品推荐的效果。

Abstract

After several years of rapid development

the collaborative filtering algorithms based on graph convolutional neural networks have achieved the most advanced performance in many recommender system scenarios.However

most of these algorithms only use simple random negative sampling method when collecting negative samples

and do not make full use of graph structure information.To solve this problem

a graph convolution neural network for recommendation using graph negative sampling(GCN-GNS) is proposed.The algorithm first constructs a user-item bipartite graph and uses a graph convolution neural network to obtain the node embedding vector.Next

the depth-first random walk method is used to obtain the sequence of the wandering item nodes that includes both the neighboring item nodes and the distant item nodes.Then the attention layer is designed to learn the weights of different nodes in the walk sequence adaptively and a dynamically updated virtual negative sample is formed according to the weights.Finally

the virtual negative sample is used to train the model more efficiently.Experimental results show that the GCN-GNS performs better than other algorithms for comparison on three real public datasets in most cases

which indicates that the proposed novel graph negative sampling method can help the GCN-GNS model to make better use of the graph structure information

and ultimately improves the effect of item recommendation.

关键词

Keywords

references

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