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

Fei Xiong, Haoran Sun, Guixun Luo, Shirui Pan, Meikang Qiu, Liang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 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.

Original languageEnglish
Title of host publicationProceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
EditorsKate Larson
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2478-2486
Number of pages9
ISBN (Electronic)9781956792041
StatePublished - 2024
Event33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of
Duration: 3 Aug 20249 Aug 2024

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Country/TerritoryKorea, Republic of
CityJeju
Period3/08/249/08/24

Fingerprint

Dive into the research topics of 'Graph Attention Network with High-Order Neighbor Information Propagation for Social Recommendation'. Together they form a unique fingerprint.

Cite this