使用图负采样的图卷积神经网络推荐算法

doi:10.19665/j.issn1001-2400.20230214

Abstract

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.

Key words: recommender systems, collaborative filtering, convolutional neural networks, graph negative sampling

CLC Number:

TP181

HUANG Heyuan, MU Caihong, FANG Yunfei, LIU Yi. Graph convolution neural network for recommendation using graph negative sampling[J].Journal of Xidian University, 2024, 51(1): 86-99.

Figures/Tables 9

References 31

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数据集方法	Lastfm		Douban		Flixster
数据集方法	NDCG	Recall	NDCG	Recall	NDCG	Recall
MF^[3]	0.186 87	0.243 87	0.084 48	0.128 49	0.109 57	0.177 65
GCN^[7]	0.168 17	0.227 48	0.085 12	0.128 71	0.081 11	0.143 45
GC-MC^[19]	0.155 66	0.212 96	0.074 96	0.112 52	0.065 22	0.127 30
NGCF^[20]	0.188 49	0.251 77	0.085 25	0.127 45	0.117 43	0.196 97
DGCF^[31]	[0.203 02]	[0.262 29]	[0.098 95]	[0.145 59]	0.131 42	0.219 57
LightGCN^[21]	0.213 40	0.275 38	0.102 74	0.149 62	[0.128 11]	[0.212 95]
GCN-GNS	0.221 27	0.285 5	0.127 28	0.170 96	0.130 58	0.218 20

Graph convolution neural network for recommendation using graph negative sampling

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