Deep multi-graph neural networks with attention fusion for recommendation

计算机科学 人工智能 人工神经网络 深层神经网络 机器学习 图形 理论计算机科学
作者
Yuzhi Song,Hailiang Ye,Ming Li,Feilong Cao
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:191: 116240-116240 被引量:41
标识
DOI:10.1016/j.eswa.2021.116240
摘要

Graph neural networks (GNNs), with their promising potential to learn effective graph representation, have been widely used for recommender systems, in which the given graph data contains abundant users, items, and their historical interaction information. How to obtain preferable latent representations for both users and items is one of the key issues for GNN-based recommendation. This paper develops a novel deep GNN model with multi-graph attention fusion, MAF-GNN. This framework constructs two feature graph attention modules and a multi-scale latent features module, to generate better user and item latent features from input information. Specifically, the dual-branch residual graph attention (DBRGA) module is presented to extract neighbors’ similar features from user and item graphs effectively and easily. Then multi-scale latent matrices are captured by applying non-linear transformations which are embedded to reduce the cost of dimension selection. Furthermore, a hybrid fusion graph attention (HFGA) module is designed to obtain valuable collaborative information from the user–item interaction graph, aiming to further refine the latent embedding of users and items. Finally, the whole MAF-GNN framework is optimized by a geometric factorized regularization loss. Extensive experiment results on both synthetic and real-world datasets illustrate that MAF-GNN can achieve better recommendation performance with a certain level of interpretability than some existing approaches.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
旦丁洋发布了新的文献求助10
1秒前
Fairyvivi完成签到,获得积分20
2秒前
Jasper应助Cloud9采纳,获得10
2秒前
2秒前
2秒前
3秒前
4秒前
在水一方应助WPY采纳,获得10
5秒前
6秒前
充电宝应助skevvecl采纳,获得10
6秒前
菜鸟发布了新的文献求助10
7秒前
7秒前
柠檬水加冰完成签到,获得积分10
8秒前
隐形曼青应助DZ采纳,获得10
8秒前
8秒前
imxiaobing完成签到,获得积分10
9秒前
Fairyvivi发布了新的文献求助10
9秒前
quw88888发布了新的文献求助20
10秒前
狂风阿来完成签到 ,获得积分10
11秒前
11秒前
11秒前
11秒前
silent完成签到,获得积分10
11秒前
11秒前
12秒前
12秒前
笨笨支付宝关注了科研通微信公众号
12秒前
13秒前
13秒前
夏轩FromHard应助碎星采纳,获得10
13秒前
CipherSage应助美满水蜜桃采纳,获得10
13秒前
14秒前
water应助jiajia采纳,获得10
14秒前
14秒前
Yuw完成签到,获得积分10
14秒前
14秒前
向日葵发布了新的文献求助20
14秒前
菜鸟完成签到,获得积分10
14秒前
SciGPT应助小摩尔采纳,获得10
14秒前
余杭村王小虎完成签到,获得积分10
15秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974643
求助须知:如何正确求助?哪些是违规求助? 3519094
关于积分的说明 11196979
捐赠科研通 3255182
什么是DOI,文献DOI怎么找? 1797700
邀请新用户注册赠送积分活动 877100
科研通“疑难数据库(出版商)”最低求助积分说明 806130