基于距离自适应的图卷积方法，提升细粒度层级推荐精度
Distance-aware Self-adaptive Graph Convolution for Fine-grained Hierarchical Recommendation
- 根据层级表示距离动态调整聚合权重，优化多层信息融合
- 在Yelp上提升超5%，ML_1M数据集Recall@10提升5.58%
- 适合需要精细层次建模的推荐系统研究者
图卷积网络（GCNs）通过有效学习用户与物品节点的表示，广泛用于提升推荐准确率与性能。然而仍存在两大挑战：（1）图表示结构缺乏进一步优化；（2）对不同卷积层贡献差异关注不足。本文提出SAGCN，一种基于距离的自适应层级聚合方法，通过差异化表示度量精炼聚合过程。SAGCN引入多层次信息聚合与表示空间优化的详细策略，使模型能基于层级表示间距离学习分层嵌入权重，实现更精准的跨层信息融合，增强对层次嵌入的捕捉能力，并优化表示空间结构。同时，目标损失函数经重构以更好契合推荐任务。在四个真实数据集上的大量实验表明，该方法显著提升性能，包括Yelp上超过5%的提升，以及在ML_1M数据集上Recall@10提高5.58%。
原文摘要 · Abstract (English)
Graph Convolutional Networks (GCNs) are widely used to improve recommendation accuracy and performance by effectively learning the representations of user and item nodes. However, two major challenges remain: (1) the lack of further optimization in the graph representation structure and (2) insufficient attention given to the varying contributions of different convolutional layers.This paper proposes SAGCN, a distance-based adaptive hierarchical aggregation method that refines the aggregation process through differentiated representation metrics. SAGCN introduces a detailed approach to multilayer information aggregation and representation space optimization, enabling the model to learn hierarchical embedding weights based on the distance between hierarchical representations. This innovation allows for more precise cross-layer information aggregation, improves the model's ability to capture hierarchical embeddings, and optimizes the representation space structure. Additionally, the objective loss function is refined to better align with recommendation tasks.Extensive experiments conducted on four real-world datasets demonstrate significant improvements, including over a 5% increase on Yelp and a 5.58% increase in Recall@10 on the ML_1M dataset.
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