TY - GEN
T1 - Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation
AU - Xiong, Fei
AU - Sun, Haoran
AU - Luo, Guixun
AU - Pan, Shirui
AU - Qiu, Meikang
AU - Wang, Liang
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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 leading 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.
AB - 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 leading 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.
UR - http://www.scopus.com/inward/record.url?scp=85204314093&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:85204314093
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2478
EP - 2486
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -