Zhang, YYu, JLiu, ZWang, GNguyen, MSheng, QZWang, N2025-03-112025-03-112025-02-24Neural Computing and Applications, ISSN: 0941-0643 (Print); 1433-3058 (Online), Springer Science and Business Media LLC, 1-20. doi: 10.1007/s00521-025-11079-80941-06431433-3058http://hdl.handle.net/10292/18847Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences4611 Machine LearningBioengineeringMachine Learning and Artificial IntelligenceNetworking and Information Technology R&D (NITRD)0801 Artificial Intelligence and Image Processing0906 Electrical and Electronic Engineering1702 Cognitive SciencesArtificial Intelligence & Image Processing4602 Artificial intelligence4603 Computer vision and multimedia computation4611 Machine learningJournal ArticleOpenAccess10.1007/s00521-025-11079-8