Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation

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.