摘要
为了缓解推荐系统中不同用户社交空间与兴趣空间的内在信息差异和忽视高阶邻居的问题,提出了一种融合用户社交关系的自适应图卷积推荐算法(adaptive graph convolutional recommendation algorithm integrating user social relationships,AGCRSR)。首先,模型在嵌入层使用映射矩阵将初始特征向量转换为自适应嵌入;其次,引入注意力机制聚合不同方面的用户嵌入,通过图卷积网络来线性学习用户和项目的潜在表示;最后,通过自适应模块聚合用户表示并利用内积函数预测用户对项目的最终推荐结果。在数据集LastFM和Ciao上与其他基线算法进行了对比实验,实验结果表明AGCRSR的推荐效果较其他算法有显著提升。
In order to alleviate the inherent information differences between different user social spaces and interest spaces in recommendation systems and the problem of ignoring high-order neighbors,this paper proposed AGCRSR.Firstly,the algorithm used a mapping matrix in the embedding layer to convert the initial feature vectors into adaptive embedding.Secondly,it introduced an attention mechanism to aggregate different aspects of user embeddings,and used a graph convolutional network to li-nearly learn the potential representations of users and items.Finally,it used the adaptive module to aggregate user representations and predict the final recommendation results of users for the project using the inner product function.This paper conducted comparative experiments on the LastFM and Ciao datasets and compared with other baseline algorithms.The experimental results show that the recommendation performance of the AGCRSR is significantly improved compared to other algorithms.
作者
王光
尹凯
Wang Guang;Yin Kai(School of Software,Liaoning Technical University,Huludao Liaoning 125105,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第2期482-487,共6页
Application Research of Computers
基金
国家自然科学基金资助项目(62173171)。
关键词
图卷积神经网络
注意力机制
社交关系
推荐系统
graph convolutional neural network
attention mechanism
social relationships
recommendation system
作者简介
王光(1979-),男,山东邹城人,副教授,硕导,硕士,主要研究方向为图神经网络、推荐系统;通信作者:尹凯(1997-),男,河南洛阳人,硕士研究生,主要研究方向为图神经网络、推荐系统(1424195159@qq.com).
