SGNNRec: A Scalable Double-Layer Attention-Based Graph Neural Network Recommendation Model

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jing He, Le Tang, Dan Tang, Ping Wang, Li Cai
{"title":"SGNNRec: A Scalable Double-Layer Attention-Based Graph Neural Network Recommendation Model","authors":"Jing He, Le Tang, Dan Tang, Ping Wang, Li Cai","doi":"10.1007/s11063-024-11555-7","DOIUrl":null,"url":null,"abstract":"<p>Due to the information from the multi-relationship graphs is difficult to aggregate, the graph neural network recommendation model focuses on single-relational graphs (e.g., the user-item rating bipartite graph and user-user social relationship graphs). However, existing graph neural network recommendation models have insufficient flexibility. The recommendation accuracy instead decreases when low-quality auxiliary information is aggregated in the recommendation model. This paper proposes a scalable graph neural network recommendation model named SGNNRec. SGNNRec fuse a variety of auxiliary information (e.g., user social information, item tag information and user-item interaction information) beside user-item rating as supplements to solve the problem of data sparsity. A tag cluster-based item-semantic graph method and an apriori algorithm-based user-item interaction graph method are proposed to realize the construction of graph relations. Furthermore, a double-layer attention network is designed to learn the influence of latent factors. Thus, the latent factors are to be optimized to obtain the best recommendation results. Empirical results on real-world datasets verify the effectiveness of our model. SGNNRec can reduce the influence of poor auxiliary information; moreover, with increasing the number of auxiliary information, the model accuracy improves.</p>","PeriodicalId":51144,"journal":{"name":"Neural Processing Letters","volume":"25 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Processing Letters","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11063-024-11555-7","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Due to the information from the multi-relationship graphs is difficult to aggregate, the graph neural network recommendation model focuses on single-relational graphs (e.g., the user-item rating bipartite graph and user-user social relationship graphs). However, existing graph neural network recommendation models have insufficient flexibility. The recommendation accuracy instead decreases when low-quality auxiliary information is aggregated in the recommendation model. This paper proposes a scalable graph neural network recommendation model named SGNNRec. SGNNRec fuse a variety of auxiliary information (e.g., user social information, item tag information and user-item interaction information) beside user-item rating as supplements to solve the problem of data sparsity. A tag cluster-based item-semantic graph method and an apriori algorithm-based user-item interaction graph method are proposed to realize the construction of graph relations. Furthermore, a double-layer attention network is designed to learn the influence of latent factors. Thus, the latent factors are to be optimized to obtain the best recommendation results. Empirical results on real-world datasets verify the effectiveness of our model. SGNNRec can reduce the influence of poor auxiliary information; moreover, with increasing the number of auxiliary information, the model accuracy improves.

SGNNRec:基于注意力的可扩展双层图神经网络推荐模型
由于多关系图中的信息难以汇总,图神经网络推荐模型主要集中在单关系图(如用户-物品评分双元图和用户-用户社会关系图)上。然而,现有的图神经网络推荐模型灵活性不足。当低质量的辅助信息被聚合到推荐模型中时,推荐准确率反而会降低。本文提出了一种名为 SGNNRec 的可扩展图神经网络推荐模型。SGNNRec 除用户-物品评分外,还融合了多种辅助信息(如用户社交信息、物品标签信息和用户-物品交互信息)作为补充,以解决数据稀疏的问题。为实现图关系的构建,提出了基于标签集群的物品语义图方法和基于apriori算法的用户-物品交互图方法。此外,还设计了双层注意力网络来学习潜在因素的影响。因此,需要对潜在因素进行优化,以获得最佳推荐结果。实际数据集的经验结果验证了我们模型的有效性。SGNNRec 可以减少不良辅助信息的影响;此外,随着辅助信息数量的增加,模型的准确性也会提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信