Enhancing Recommendation Performance Using Attribute-Aware Message-Passing and Augmentation GCN

Graph Convolutional Networks (GCNs) have shown great promise in recommender systems due to their ability to capture complex relationships and generate high-quality representations, especially under sparse data conditions. However, stacking multiple GCN layers often leads to oversmoothing, where node embeddings become indistinguishably similar. This problem is exacerbated when target items gather noisy or irrelevant information from high-order neighbors during message propagation. To address this, we propose AMP-GCN, an Attribute-aware Message-Passing GCN that mitigates oversmoothing by clustering items with similar attributes into subgraphs. High-order propagation is then performed within each subgraph, effectively filtering out irrelevant signals and preserving semantic consistency. To further enhance embedding learning, we introduce AMPA-GCN, which integrates item-item correlation signals into the AMP-GCN framework by modifying the adjacency matrix. This design strengthens direct and indirect item relations, leading to more robust representations. Extensive experiments on four public benchmark datasets demonstrate that our proposed models consistently outperform state-of-the-art baselines. The source code and implementation details are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xiaolutihua/AMPA_GCN</uri>.