{"type":"ScholarlyArticle","title":"Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation","authors":[{"name":"Fei Xiong","slug":"fei-xiong","given":"Fei","family":"Xiong","affiliation":"School of Electronic and Information Engineering , Beijing Jiaotong University"},{"name":"Haoran Sun","slug":"haoran-sun","given":"Haoran","family":"Sun","affiliation":"School of Electronic and Information Engineering , Beijing Jiaotong University"},{"name":"Guixun Luo","slug":"guixun-luo","given":"Guixun","family":"Luo","affiliation":"School of Computer Science and Technology , Beijing Jiaotong University"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"School of Information and Communication Technology , Griffith University"},{"name":"Meikang Qiu","slug":"meikang-qiu","given":"Meikang","family":"Qiu","affiliation":"School of Computer and Cyber Sciences , Augusta University"},{"name":"Liang Wang","slug":"liang-wang","given":"Liang","family":"Wang","affiliation":"School of Computer Science , Northwestern Polytechnical University"}],"abstract":"In recommender systems, graph neural networks (GNN) can integrate interactions between users and items with their attributes, which makes GNN-based methods more powerful. However, directly stacking multiple layers in a graph neural network can easily lead to over-smoothing, hence recommendation systems based on graph neural networks typically underutilize higher-order neighborhoods in their learning. Although some heterogeneous graph random walk methods based on meta-paths can achieve higher-order aggregation, the focus is predominantly on the nodes at the ends of the paths. Moreover, these methods require manually defined meta-paths, which limits the model’s expressiveness and flexibility. Furthermore, path encoding in graph neural networks usually focuses only on the sequence leading to the target node. However, real-world interactions often do not follow this strict sequence, limiting the predictive performance of sequence-based network models. These problems prevent GNN-based methods from being fully effective. We propose a Graph Attention network with Information Propagation path aggregation for Social Recommendation (GAIPSRec). Firstly, we propose a universal heterogeneous graph sampling framework that does not require manually defining meta-paths for path sampling, thereby offering greater flexibility. Moreover, our method takes into account all nodes on the aggregation path and is capable of learning information from higher-order neighbors without succumbing to over-smoothing. Finally, our method utilizes a gate mechanism to fuse sequential and non-sequential dependence in encoding path instances, allowing a more holistic view of the data. Extensive experiments on real-world datasets show that our proposed GAIPSRec improves the performance significantly and outperforms state-of-the-art methods.","doi":"10.24963/ijcai.2024/274","url":"https://ijcai.org/proceedings/2024/274","pdf":"https://ijcai.org/proceedings/2024/0274.pdf","markdown":"https://ijcai.org/proceedings/2024/274.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/274","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":274,"pages":{"first":2478,"last":2486},"keywords":["Data Mining: DM: Mining graphs","Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Bruna et al., 2013] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 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