Survey of Graph Neural Network in Recommendation System

doi:10.3778/j.issn.1673-9418.2203004

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2249-2263.DOI: 10.3778/j.issn.1673-9418.2203004

• Surveys and Frontiers • Previous Articles Next Articles

Survey of Graph Neural Network in Recommendation System

WU Jing, XIE Hui⁺(), JIANG Huowen

School of Mathematics and Computer Science, Jiangxi Science and Technology Normal University, Nanchang 330038, China

Received:2022-03-01 Revised:2022-06-07 Online:2022-10-01 Published:2022-06-15
About author:WU Jing, born in 1997, M.S. candidate, student member of CCF. Her research interests include re-commendation system and graph neural networks.
XIE Hui, born in 1978, Ph.D., associate profes-sor, member of CCF. His research interests in-clude data mining and intelligent recommen-dation.
JIANG Huowen, born in 1974, Ph.D., profes-sor, member of CCF. His research interests inclu-de privacy preservation and computer education.
Supported by:
National Natural Science Foundation of China(71561013);National Natural Science Foundation of China(61762044);Social Science Planning Projects in Jiangxi Province(20TQ04);Fund of Humanities and Social Sciences in Universities of Jiangxi Province(JC17221);Fund of Humanities and Social Sciences in Universities of Jiangxi Province(JD18083);Fund of Humanities and Social Sciences in Universities of Jiangxi Province(JC18109);Project of Science and Technology Plan by Education Department of Jiangxi Province(GJJ211116);Project of Science and Technology Plan by Education Department of Jiangxi Province(GJJ170661)

图神经网络推荐系统综述

吴静, 谢辉⁺(), 姜火文

江西科技师范大学数学与计算机科学学院,南昌 330038

通讯作者: + E-mail: huixie@aliyun.com
作者简介:吴静（1997—）,女,江西九江人,硕士研究生,CCF学生会员,主要研究方向为推荐系统、图神经网络。
谢辉（1978—）,男,河南信阳人,博士,副教授,CCF会员,主要研究方向为数据挖掘、智能推荐。
姜火文（1974—）,男,江西进贤人,博士,教授,CCF会员,主要研究方向为隐私保护、计算机教育。
基金资助:
国家自然科学基金(71561013);国家自然科学基金(61762044);江西省社会科学研究规划项目(20TQ04);江西省高校人文社会科学研究项目(JC17221);江西省高校人文社会科学研究项目(JD18083);江西省高校人文社会科学研究项目(JC18109);江西省教育厅科技计划项目(GJJ211116);江西省教育厅科技计划项目(GJJ170661)

Abstract

Abstract:

Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well. Graph neural network (GNN) can extract and represent the features from edges and nodes data in the graphs and has inherent advantages in processing the graphs structure data, so it flourishes in recommendation system. This paper sorts out the main references of graph neural network in recommendation system in recent years, focuses on the two perspectives of method and problem, and systematically reviews graph neural network in recommendation system. Firstly, from the method level, five graph neural networks of the recommendation system are elaborated, including the graph convolutional network in the recommendation system, graph attention network in the recommendation system, graph autoencoder in the recommendation system, graph generation network in the recommendation system and graph spatial-temporal network in the recommendation system. Secondly, from the perspective of problem similarity, six major problem types are summarized: sequence recommendation, social recommendation, cross-domain recommendation, multi-behavior recommendation, bundle recommendation, and session-based recommen-dation. Finally, based on the analysis and summary of the existing methods, this paper points out the main difficu-lties in the current research on graph neural network in recommendation system, proposes the corresponding issues that can be investigated, and looks forward to the future research directions on this topic.

Key words: graph neural network (GNN), recommendation system (RS), graph convolution network (GCN)

摘要：

推荐系统（RS）因信息冗杂繁多而诞生。由于数据形式的多样化、复杂化以及数据信息量稀疏性,传统的推荐系统已经不能很好地解决目前的问题。图神经网络（GNN）能从图中对边和节点数据进行特征提取和表示,对处理图结构数据具有先天优势,因此在推荐系统中蓬勃发展。将近年的主要研究成果进行了梳理并加以总结,着重从方法、问题两个角度出发,系统性地综述了图神经网络推荐系统。首先,从方法层面阐述了图卷积网络推荐系统、图注意力网络推荐系统、图自动编码器推荐系统、图生成网络推荐系统、图时空网络推荐系统等五大类的图神经网络推荐系统;接着,从问题相似性出发,归纳出序列推荐问题、社交推荐问题、跨域推荐问题、多行为推荐问题、捆绑推荐问题以及基于会话推荐问题等六大类问题;最后,在对已有方法分析和总结的基础上,指出了目前图神经网络推荐系统研究面临的难点,提出相应的研究问题以及未来研究的方向。

关键词: 图神经网络（GNN）, 推荐系统（RS）, 图卷积网络（GCN）

CLC Number:

TP391

WU Jing, XIE Hui, JIANG Huowen. Survey of Graph Neural Network in Recommendation System[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2249-2263.

吴静, 谢辉, 姜火文. 图神经网络推荐系统综述[J]. 计算机科学与探索, 2022, 16(10): 2249-2263.

Figures/Tables 5

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分类	作者	关键技术	问题场景	优点	局限性
图卷积网络推荐系统	Ying等^[11]	GCN、随机游走	Web推荐任务	提高模型的鲁棒性	不能解决其他大规模的图表示学习问题
	Chen等^[12]	GCN	所有推荐任务	减少处理延迟	内存访问模型复杂
	Tran等^[13]	GCN	应用于大规模异构图数据的推荐任务	处理异构图数据	仅适用于两个实体,即用户和项目
	Shafqat等^[14]	GCN	在线产品推荐任务	简化了GCN模型的邻居抽样任务,提高了训练效率,降低了复杂度和计算时间	需要形成会话图,并不适应于所有推荐系统场景
	Yin等^[15]	GCN	异构信息网络的推荐任务	提取和组合异构图中的结构特征,减小了训练规模,提高了计算效率	算法复杂
	Chen等^[16]	GCN、KG	TOP-K推荐	提高可解释性	学习效率低,未利用更多的辅助信息
	Bonet等^[17]	GCN、递归神经网络	大数据推荐任务	提高推荐系统的性能和推荐的准确度	处理不了冷启动和数据稀疏性问题,忽略了推荐系统的可解释性
图注意力网络推荐系统	Song等^[18]	图注意力神经网络	在线社区社交推荐	能进行用户的动态的兴趣推荐	只能对大规模数据有效
	Jiang等^[19]	图注意力神经网络、GCN	社交推荐	能发现潜在的社会传播效应	模型复杂,无法区分社交的正面和负面影响
	Wu等^[20]	图注意力神经网络	社交推荐	能学习社会深层次表征,提高推荐准确度	需要提取足够多的高层联系信息
	Xiao等^[21]	图注意力神经网络	社交推荐	融合用户偏好和社交交互信息	不能完全利用辅助信息
	Dang等^[22]	图注意力神经网络、知识图谱	Web服务	充分挖掘文本特征,解决数据稀疏性问题,优化特征表示,提高推荐的可解释性	模型需与其他开放知识库相结合
	Li等^[23]	图注意力神经网络、知识图谱	评级预测任务、TOP-K推荐任务	解决数据稀疏和冷启动的问题	运行时间较长
	Salamat等^[24]	图注意力神经网络	社交推荐	提高了模型的可解释性	未考虑社交网络的动态行为
	Sang等^[25]	图注意力神经网络、知识图谱、残差递归神经网络	所有推荐	能自动捕捉丰富的语义信息和用户与项目之间复杂的隐含关系	未考虑用户之间交互的顺序性
图自动编码器推荐系统	Zheng等^[29]	图自动编码器、GCN	社交推荐	捕捉隐藏在图结构下的隐式高阶关系,提高推荐系统性能	未考虑用户之间交互的顺序性
	Yao等^[30]	图自动编码器、GCN	隐式数据的推荐系统	捕获数据相关性以提高推荐性能	未考虑时间顺序因素
	Deng等^[31]	图自动编码器、无监督学习、有监督学习	会话推荐	考虑了会话中的项目之间依赖关系	对模型中各组件和超参数的影响未知
	Ohtomo等^[32]	图自动编码器	个性化推荐	从大量帖子中为每个用户个性化推荐帖子	训练时间长
图生成网络推荐系统	Zhou等^[34]	图生成神经网络、GCN	个性化推荐	更好地利用辅助信息并生成不受限制的输出表示	对稀疏性数据很容易产生过拟合
	Xu等^[35]	图生成神经网络、GCN	社交推荐	解决冷启动问题	大图的计算复杂度高
	Wu等^[36]	图生成神经网络、生成对抗网络	在线推荐	增强推荐系统的稳定性	只能对评论等基于内容的推荐有用
	Zhang等^[37]	图生成神经网络、GCN	图像推荐	解决了合成细粒度纹理和小规模实例的困难	严重依赖于推断的语义
	Xu等^[38]	图生成神经网络、GCN	在线视频推荐	提高推荐的准确度	大图长序列建模困难,信息质量要求高
图时空网络推荐系统	Park等^[39]	图时空神经网络、GCN	运动风格推荐	能提取空间和时间两个维度的特征	对随机噪音十分敏感,适合少量已经标好明确样式标签的数据
	Zhang等^[40]	图时空神经网络、图嵌入、GCN	所有推荐任务	适用性广	仅仅考虑了二部图,未扩展到多部异构图而且训练过程中提取数据是均匀抽样,其实用性较差
	杨珍等^[41]	图时空神经网络、GCN	用户商品推荐	提高了推荐系统的性能	只能用于购物商品推荐
	Han等^[42]	图时空神经网络、GCN	POI推荐	缓解数据稀疏性问题	未能考虑到时空序列节点之间的上下文信息

分类	作者	关键技术	问题场景	优点	局限性
图卷积网络推荐系统	Ying等^[11]	GCN、随机游走	Web推荐任务	提高模型的鲁棒性	不能解决其他大规模的图表示学习问题
	Chen等^[12]	GCN	所有推荐任务	减少处理延迟	内存访问模型复杂
	Tran等^[13]	GCN	应用于大规模异构图数据的推荐任务	处理异构图数据	仅适用于两个实体,即用户和项目
	Shafqat等^[14]	GCN	在线产品推荐任务	简化了GCN模型的邻居抽样任务,提高了训练效率,降低了复杂度和计算时间	需要形成会话图,并不适应于所有推荐系统场景
	Yin等^[15]	GCN	异构信息网络的推荐任务	提取和组合异构图中的结构特征,减小了训练规模,提高了计算效率	算法复杂
	Chen等^[16]	GCN、KG	TOP-K推荐	提高可解释性	学习效率低,未利用更多的辅助信息
	Bonet等^[17]	GCN、递归神经网络	大数据推荐任务	提高推荐系统的性能和推荐的准确度	处理不了冷启动和数据稀疏性问题,忽略了推荐系统的可解释性
图注意力网络推荐系统	Song等^[18]	图注意力神经网络	在线社区社交推荐	能进行用户的动态的兴趣推荐	只能对大规模数据有效
	Jiang等^[19]	图注意力神经网络、GCN	社交推荐	能发现潜在的社会传播效应	模型复杂,无法区分社交的正面和负面影响
	Wu等^[20]	图注意力神经网络	社交推荐	能学习社会深层次表征,提高推荐准确度	需要提取足够多的高层联系信息
	Xiao等^[21]	图注意力神经网络	社交推荐	融合用户偏好和社交交互信息	不能完全利用辅助信息
	Dang等^[22]	图注意力神经网络、知识图谱	Web服务	充分挖掘文本特征,解决数据稀疏性问题,优化特征表示,提高推荐的可解释性	模型需与其他开放知识库相结合
	Li等^[23]	图注意力神经网络、知识图谱	评级预测任务、TOP-K推荐任务	解决数据稀疏和冷启动的问题	运行时间较长
	Salamat等^[24]	图注意力神经网络	社交推荐	提高了模型的可解释性	未考虑社交网络的动态行为
	Sang等^[25]	图注意力神经网络、知识图谱、残差递归神经网络	所有推荐	能自动捕捉丰富的语义信息和用户与项目之间复杂的隐含关系	未考虑用户之间交互的顺序性
图自动编码器推荐系统	Zheng等^[29]	图自动编码器、GCN	社交推荐	捕捉隐藏在图结构下的隐式高阶关系,提高推荐系统性能	未考虑用户之间交互的顺序性
	Yao等^[30]	图自动编码器、GCN	隐式数据的推荐系统	捕获数据相关性以提高推荐性能	未考虑时间顺序因素
	Deng等^[31]	图自动编码器、无监督学习、有监督学习	会话推荐	考虑了会话中的项目之间依赖关系	对模型中各组件和超参数的影响未知
	Ohtomo等^[32]	图自动编码器	个性化推荐	从大量帖子中为每个用户个性化推荐帖子	训练时间长
图生成网络推荐系统	Zhou等^[34]	图生成神经网络、GCN	个性化推荐	更好地利用辅助信息并生成不受限制的输出表示	对稀疏性数据很容易产生过拟合
	Xu等^[35]	图生成神经网络、GCN	社交推荐	解决冷启动问题	大图的计算复杂度高
	Wu等^[36]	图生成神经网络、生成对抗网络	在线推荐	增强推荐系统的稳定性	只能对评论等基于内容的推荐有用
	Zhang等^[37]	图生成神经网络、GCN	图像推荐	解决了合成细粒度纹理和小规模实例的困难	严重依赖于推断的语义
	Xu等^[38]	图生成神经网络、GCN	在线视频推荐	提高推荐的准确度	大图长序列建模困难,信息质量要求高
图时空网络推荐系统	Park等^[39]	图时空神经网络、GCN	运动风格推荐	能提取空间和时间两个维度的特征	对随机噪音十分敏感,适合少量已经标好明确样式标签的数据
	Zhang等^[40]	图时空神经网络、图嵌入、GCN	所有推荐任务	适用性广	仅仅考虑了二部图,未扩展到多部异构图而且训练过程中提取数据是均匀抽样,其实用性较差
	杨珍等^[41]	图时空神经网络、GCN	用户商品推荐	提高了推荐系统的性能	只能用于购物商品推荐
	Han等^[42]	图时空神经网络、GCN	POI推荐	缓解数据稀疏性问题	未能考虑到时空序列节点之间的上下文信息

问题分类	方法分类	作者	难点
序列推荐问题	图注意力网络、图卷积网络	Yang等^[45]	数据稀疏和冷启动问题,异构图
	图卷积网络、图注意力网络	Gu等^[46]	动态兴趣建模问题
	图注意力网络	Tao等^[47]	项目趋势信息,动态图构建问题
	图注意力网络	Wang等^[48]	高阶关系建模,可解释性
社交推荐问题	图自动编码器	Guo等^[49]	大数据与个性化信息
	图神经网络	Liu等^[50]	大数据,关系动态变化问题
	图注意力网络	Salamat等^[51]	大数据,异构图,可解释性,动态行为
	图注意力网络	Liu等^[52]	动态表示问题,知识图
	图注意力网络	Tu等^[53]	数据稀疏,个性化问题,知识图
	图卷积网络	Wang等^[54]	大数据,隐含兴趣,动态兴趣
跨域推荐问题	图神经网络	Yang等^[55]	大数据,数据稀疏和冷启动,动态问题
	图神经网络	Loannidis等^[56]	可解释性
	图神经网络	Ouyang等^[57]	数据稀疏
	图注意力网络	Sheu等^[58]	缺乏用户交互记录
	图神经网络	Liang等^[59]	信息高效性,异构图
	图卷积网络、图注意力网络	Ma等^[60]	异构图,多样性和准确性
	图卷积网络	Wang等^[61]	交互图嵌入特征表示
	图卷积网络	He等^[62]	邻域聚合
	图神经网络	Amar^[63]	算法简洁性,信息高效性
	图神经网络	Liu等^[64]	模型精确性
多行为推荐问题	图神经网络	Xia等^[65]	提取多类型下的异构关系
	图神经网络	Yu等^[66]	有效捕获信息
	图卷积网络、图注意力网络	Ma等^[60]	异构图,多样性和准确性
捆绑推荐问题	图神经网络	Yang等^[67]	信息增强问题
	图神经网络	Zhang等^[68]	异构图
	图注意力网络	Yuan等^[69]	异构图
	图神经网络	Liu等^[70]	个性多样化
	图神经网络	Chen等^[71]	动态化,准确性
	图注意力网络、图卷积网络	Yang等^[45]	数据稀疏和冷启动问题,异构图
	图卷积网络	Gong等^[72]	结合深度学习从舞蹈动作中推荐音乐
	图神经网络	Ling等^[73]	信息的高阶连通性
	图卷积网络、图自动编码器	Zhang等^[74]	大数据,数据稀疏
	图神经网络	Zhu等^[75]	数据稀疏和冷启动问题
会话推荐问题	图神经网络	Zheng等^[76]	异构图,潜在信息
	图神经网络	Yu等^[66]	有效捕获信息
	图卷积网络、图注意力网络	Gu等^[46]	动态兴趣建模问题
	图神经网络	Huang等^[77]	动态信息及信息增强

问题分类	方法分类	作者	难点
序列推荐问题	图注意力网络、图卷积网络	Yang等^[45]	数据稀疏和冷启动问题,异构图
	图卷积网络、图注意力网络	Gu等^[46]	动态兴趣建模问题
	图注意力网络	Tao等^[47]	项目趋势信息,动态图构建问题
	图注意力网络	Wang等^[48]	高阶关系建模,可解释性
社交推荐问题	图自动编码器	Guo等^[49]	大数据与个性化信息
	图神经网络	Liu等^[50]	大数据,关系动态变化问题
	图注意力网络	Salamat等^[51]	大数据,异构图,可解释性,动态行为
	图注意力网络	Liu等^[52]	动态表示问题,知识图
	图注意力网络	Tu等^[53]	数据稀疏,个性化问题,知识图
	图卷积网络	Wang等^[54]	大数据,隐含兴趣,动态兴趣
跨域推荐问题	图神经网络	Yang等^[55]	大数据,数据稀疏和冷启动,动态问题
	图神经网络	Loannidis等^[56]	可解释性
	图神经网络	Ouyang等^[57]	数据稀疏
	图注意力网络	Sheu等^[58]	缺乏用户交互记录
	图神经网络	Liang等^[59]	信息高效性,异构图
	图卷积网络、图注意力网络	Ma等^[60]	异构图,多样性和准确性
	图卷积网络	Wang等^[61]	交互图嵌入特征表示
	图卷积网络	He等^[62]	邻域聚合
	图神经网络	Amar^[63]	算法简洁性,信息高效性
	图神经网络	Liu等^[64]	模型精确性
多行为推荐问题	图神经网络	Xia等^[65]	提取多类型下的异构关系
	图神经网络	Yu等^[66]	有效捕获信息
	图卷积网络、图注意力网络	Ma等^[60]	异构图,多样性和准确性
捆绑推荐问题	图神经网络	Yang等^[67]	信息增强问题
	图神经网络	Zhang等^[68]	异构图
	图注意力网络	Yuan等^[69]	异构图
	图神经网络	Liu等^[70]	个性多样化
	图神经网络	Chen等^[71]	动态化,准确性
	图注意力网络、图卷积网络	Yang等^[45]	数据稀疏和冷启动问题,异构图
	图卷积网络	Gong等^[72]	结合深度学习从舞蹈动作中推荐音乐
	图神经网络	Ling等^[73]	信息的高阶连通性
	图卷积网络、图自动编码器	Zhang等^[74]	大数据,数据稀疏
	图神经网络	Zhu等^[75]	数据稀疏和冷启动问题
会话推荐问题	图神经网络	Zheng等^[76]	异构图,潜在信息
	图神经网络	Yu等^[66]	有效捕获信息
	图卷积网络、图注意力网络	Gu等^[46]	动态兴趣建模问题
	图神经网络	Huang等^[77]	动态信息及信息增强

Survey of Graph Neural Network in Recommendation System

图神经网络推荐系统综述

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[1]	JIANG Guangfeng, HU Pengcheng, YE Hua, YANG Yanlan. Isomorphic Graph Classification Model Based on Reconstruction Error [J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(1): 185-193.
[2]	WANG Xiaodong, ZHAO Yining, XIAO Haili, WANG Xiaoning, CHI Xuebin. User Behavior Analysis with RNN and Graph Neural Networks [J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(5): 838-847.