Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation

计算机科学 人工智能 机器学习 特征学习 推荐系统 图形 判别式 自编码 深度学习 自然语言处理 理论计算机科学
作者
Ke Wang,Yanmin Zhu,Tianzi Zang,Chunyang Wang,Kuan Liu,Peibo Ma
出处
期刊:ACM Transactions on Information Systems [Association for Computing Machinery]
卷期号:42 (2): 1-29 被引量:2
标识
DOI:10.1145/3618106
摘要

Review-based recommender systems explore semantic aspects of users’ preferences by incorporating user-generated reviews into rating-based models. Recent works have demonstrated the potential of review information to improve the recommendation capacity. However, most existing studies rely on optimizing review-based representation learning part, thus failing to explicitly capture the fine-grained semantic aspects, and also ignoring the intrinsic correlation between ratings and reviews. To address these problems, we propose a multi-aspect graph contrastive learning framework, named MAGCL, with three distinctive designs: (i) a multi-aspect representation learning module, which projects semantic relations to different subspaces by decoupling review information, and then obtains high-order decoupled representations in each aspect via graph encoder. (ii) the contrastive learning module performs graph contrastive learning to capture the correlation between rating and review patterns, which utilize unlabeled data to generate self-supervised signals and, in turn, relieve the data sparsity problem of supervision signals. (iii) the multi-task learning module conducts joint training to learn high-order structure-aware yet self-discriminative node representations by combining recommendation task and self-supervised task, which helps alleviate the over-smoothing problem. Extensive experiments are conducted on four real-world review datasets and the results show the superiority of the proposed framework MAGCL compared with several state of the arts. We also provide further analysis on multi-aspect representations and graph contrastive learning to verify the advantage of proposed framework.
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
刚刚
1秒前
victor发布了新的文献求助30
1秒前
yiyiy1完成签到,获得积分10
1秒前
zhihan完成签到,获得积分10
1秒前
1秒前
zz发布了新的文献求助10
1秒前
希望天下0贩的0应助dearcih采纳,获得10
1秒前
小林完成签到,获得积分10
2秒前
2秒前
TRY发布了新的文献求助10
3秒前
4秒前
vvv应助高兴采纳,获得10
4秒前
4秒前
刘佳佳完成签到 ,获得积分10
5秒前
5秒前
monoklatt发布了新的文献求助10
5秒前
Kevin发布了新的文献求助10
5秒前
Orange应助樱悼柳雪采纳,获得10
6秒前
6秒前
ZYT723完成签到,获得积分10
6秒前
SH发布了新的文献求助10
7秒前
7秒前
顾矜应助彩色蘑菇采纳,获得10
7秒前
sisyphus_yy完成签到 ,获得积分10
8秒前
轻松的惜芹应助Djtc采纳,获得20
8秒前
9秒前
Donby发布了新的文献求助10
9秒前
于浩完成签到 ,获得积分10
9秒前
QZZ发布了新的文献求助10
9秒前
10秒前
mzf发布了新的文献求助10
10秒前
zz完成签到,获得积分10
11秒前
11秒前
SciGPT应助wjx采纳,获得10
11秒前
11秒前
SHD发布了新的文献求助10
11秒前
清爽玉米完成签到,获得积分10
12秒前
vialavilda发布了新的文献求助10
12秒前
12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 3974856
求助须知:如何正确求助?哪些是违规求助? 3519400
关于积分的说明 11198085
捐赠科研通 3255563
什么是DOI,文献DOI怎么找? 1797860
邀请新用户注册赠送积分活动 877208
科研通“疑难数据库(出版商)”最低求助积分说明 806219