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Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

文献类型：期刊论文

作者Huang, Junjie3,4; Xie, Ruobing1; Cao, Qi3,4; Shen, Huawei3,4; Zhang, Shaoliang1; Xia, Feng1; Cheng, Xueqi2
刊名INFORMATION PROCESSING & MANAGEMENT
出版日期2023-07-01
卷号60期号:4页码:14
关键词Negative feedback Signed social networks Graph Neural Networks Recommender system
ISSN号0306-4573
DOI10.1016/j.ipm.2023.103403
英文摘要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.
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
语种英语
WOS记录号WOS:001004629800001
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/21201]  
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.Tencent, WeChat Search Applicat Dept, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing, Peoples R China
4.Univ Chinese Acad Sci, 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.

入库方式： OAI收割

来源：计算技术研究所

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