[1]吴长旺,黄 刚,胡婷婷.融合注意力机制的 GNN 推荐算法[J].计算机技术与发展,2022,32(10):7-13.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 002]
　WU Chang-wang,HUANG Gang,HU Ting-ting.GNN Recommendation Algorithm Fused with Attention Mechanism[J].,2022,32(10):7-13.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 002]
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融合注意力机制的 GNN 推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
7-13
栏目:
大数据与云计算
出版日期:
2022-10-10

文章信息/Info

Title:
GNN Recommendation Algorithm Fused with Attention Mechanism
文章编号:
1673-629X(2022)10-0007-07
作者:
吴长旺; 黄 刚; 胡婷婷
南京邮电大学 计算机学院、软件学院、网络空间安全学院,江苏 南京 210023
Author(s):
WU Chang-wang; HUANG Gang; HU Ting-ting
School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
图神经网络; 注意力机制; 门控循环单元; 社交信息; 推荐系统
Keywords:
graph neural network; attention mechanism; gated recurrent unit; social information; recommended system
分类号:
TP309
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 002
摘要:
在推荐系统中,用户的兴趣爱好受到自身的历史行为、社交网络等多方面影响,呈现出一种动态变化的趋势。 而如何在推荐系统中结合用户的社交网络信息以及时序兴趣提取有效信息,是一个棘手的问题。对此,提出了一种融合多头注意力机制和门控循环单元的图神经网络算法 MGRU。 该算法先利用门控循环单元对时序信息进行记忆与遗忘,从而增强局部图邻域迭代过程中时序信息的抽象能力。 再利用注意力记忆网络获得朋友在不同方面对用户的影响,依靠多头注意力机制来调节朋友的影响力大小。 通过门控神经网络将朋友的影响和用户自身偏好进行融合,对项目进行推荐。 在Ciao 与 Epionions 数据集上使用均方根误差和平均绝对误差作为评价指标进行实验,结果证明该算法提升了推荐系统的准确率。
Abstract:
In the recommendation system,the user’s interests and hobbies are affected by their own historical behavior,social networks and other aspects,showing a trend of dynamic changes. How to extract effective information from the user爷 s social network information and time series interests in the recommendation system is a difficulty. Therefore, a graph neural network algorithm MGRU, which combines multi-head attention mechanism and gated recurrent unit, is proposed. Firstly the gated loop unit is used to memorize and forget the time series information,there by enhancing the abstraction ability of the time series information in the iterative process of the local graph neighborhood. Then the attention memory network is applied to obtain the influence of friends on users in different aspects and adjust the influence of friends according to the multi-head attention mechanism. Through the gated neural network,the influence off riends and the user爷 s own preferences are integrated to recommend items. Experiments on the Ciao and Epionions datasets are carriedout with the root mean square error and the average absolute error as evaluation indicators. It is proved that the proposed algorithm improves the accuracy of the recommendation system.

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更新日期/Last Update: 2022-10-10