Adversarial Heterogeneous Graph Neural Network for Robust Recommendation
文献类型：期刊论文
| 作者 | Sang, Lei1; Xu, Min4; Qian, Shengsheng3 ; Wu, Xindong2
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| 刊名 | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
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| 出版日期 | 2023-05-16 |
| 页码 | 12 |
| ISSN号 | 2329-924X |
| 关键词 | Perturbation methods Motion pictures Training Graph neural networks Robustness Semantics Predictive models Adversarial training (AT) graph neural network (GNN) heterogeneous graph recommendation |
| DOI | 10.1109/TCSS.2023.3268683 |
| 通讯作者 | Xu, Min(Min.Xu@uts.edu.au) |
| 英文摘要 | Recommendation systems play a vital role in identifying the hidden interactions between users and items in online social networks. Recently, graph neural networks (GNNs) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for a recommendation. However, existing GNN-based methods do not have broad applicability to heterogeneous graphs that integrate auxiliary data with diverse types. Moreover, graph structures are susceptible to noise and even unnoticed malicious perturbations, as perturbations from connected nodes can create cumulative effects on a target node in the graph. To enhance the robustness and generalization of GNN-based recommendations, we propose a new optimization model named Adversarial Heterogeneous Graph Neural Network for RECommendation (AHGNNRec). First, AHGNNRec learns user and item embeddings by exploring the distinct contributions of various types of interactions between users and items using a hierarchical heterogeneous graph neural network (HGNN). Second, to produce more robust embeddings for recommendations, we employ the adversarial training (AT) method to optimize the HGNN layers. AT is a min-max optimization training process where the generated adversarial fake nodes from normal nodes with intentional perturbations try to maximally deteriorate the recommendation performance. Following this, we learn about these adversarial user or item nodes by minimizing the impact of an additional regularization term for the recommendation. The experimental outcomes on two real-world benchmark datasets demonstrate the effectiveness of AHGNNRec. |
| WOS关键词 | SHILLING ATTACKS ; SYSTEMS |
| 资助项目 | National Natural Science Foundation of China[62206002] ; Anhui Provincial Natural Science Foundation[2208085QF195] ; Anhui Provincial Natural Science Foundation[2208085QF199] ; Australia Research Council (ARC) Linkage Projects[LP210100129] ; Australian Research Council[LP210100129] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001004788000001 |
| 资助机构 | National Natural Science Foundation of China ; Anhui Provincial Natural Science Foundation ; Australia Research Council (ARC) Linkage Projects ; Australian Research Council |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53468]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Xu, Min |
| 作者单位 | 1.Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China 2.Hefei Univ Technol, Key Lab Knowledge Engn Big Data, Minist Educ China, Hefei 230601, Anhui, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia |
| 推荐引用方式 GB/T 7714 | Sang, Lei,Xu, Min,Qian, Shengsheng,et al. Adversarial Heterogeneous Graph Neural Network for Robust Recommendation[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2023:12. |
| APA | Sang, Lei,Xu, Min,Qian, Shengsheng,&Wu, Xindong.(2023).Adversarial Heterogeneous Graph Neural Network for Robust Recommendation.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,12. |
| MLA | Sang, Lei,et al."Adversarial Heterogeneous Graph Neural Network for Robust Recommendation".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023):12. |
入库方式： OAI收割
来源：自动化研究所
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