Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

doi:10.1016/j.ipm.2023.103403

	Negative Can Be Positive: Signed Graph Neural Networks for Recommendation
	Huang, Junjie 1,2; Xie, Ruobing 3; Cao, Qi 1,2; Shen, Huawei 1,2; Zhang, Shaoliang 3; Xia, Feng 3; Cheng, Xueqi 4
	2023-07-01
发表期刊	INFORMATION PROCESSING & MANAGEMENT
ISSN	0306-4573
卷号	60 期号:4 页码:14
摘要	Most of the existing GNN-based recommender system models focus on learning users' per-sonalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users' feedback be-havior also includes negative feedback behavior (e.g., click dislike button), which also reflects users' personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected - not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users' negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations.
关键词	Negative feedback Signed social networks Graph Neural Networks Recommender system
DOI	10.1016/j.ipm.2023.103403
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[2022YFB3103700] ; National Natural Science Foundation of China[62102402] ; National Key R&D Program of China[2022YFB3103701] ; National Key R&D Program of China[62272125] ; Beijing Academy of Artificial Intelligence (BAAI) ; Tencent Rhino-Bird Elite Training Program ; [U21B2046]
WOS研究方向	Computer Science ; Information Science & Library Science
WOS类目	Computer Science, Information Systems ; Information Science & Library Science
WOS记录号	WOS:001004629800001
出版者	ELSEVIER SCI LTD
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21202
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shen, Huawei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Tencent, WeChat Search Applicat Dept, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Huang, Junjie,Xie, Ruobing,Cao, Qi,et al. Negative Can Be Positive: Signed Graph Neural Networks for Recommendation[J]. INFORMATION PROCESSING & MANAGEMENT,2023,60(4):14.
APA	Huang, Junjie.,Xie, Ruobing.,Cao, Qi.,Shen, Huawei.,Zhang, Shaoliang.,...&Cheng, Xueqi.(2023).Negative Can Be Positive: Signed Graph Neural Networks for Recommendation.INFORMATION PROCESSING & MANAGEMENT,60(4),14.
MLA	Huang, Junjie,et al."Negative Can Be Positive: Signed Graph Neural Networks for Recommendation".INFORMATION PROCESSING & MANAGEMENT 60.4(2023):14.