神经树协同过滤：将图协同过滤重新思考为具有曲率感知传播深度的树协同过滤
Neural Tree Collaborative Filtering: Rethinking Graph Collaborative Filtering as Tree Collaborative Filtering with Curvature-Aware Propagation Depth
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中文总结 AI 辅助
该研究提出神经树协同过滤（NTCF）框架，将图协同过滤重新建模为树结构并基于曲率分配节点特定传播深度，其性能优于多数GCF模型，可作为骨干集成到自监督模型中，经多数据集实验验证了优越性。
中文摘要 AI 辅助
图协同过滤（GCF）已成为现代推荐系统中的主流范式，其将用户-物品交互建模为二分图并通过固定数量的消息传递层传播嵌入。然而，对每个节点应用统一的传播深度忽略了真实交互图的一个基本属性：节点的局部连通性存在显著差异，因此外围节点会迅速出现过平滑问题，而类枢纽节点在超出其直接邻域时仍未被充分探索。在本文中，我们从树结构视角重新审视GCF，提出神经树协同过滤（NTCF）这一框架，该框架将每个节点的局部邻域重新解释为有根树，并基于作为离散里奇曲率代理的闭式局部度不平衡分数分配节点特定的传播深度。我们提供的理论分析表明：（i）NTCF严格泛化了NGCF，当所有曲率诱导的深度调整消失时退化为NGCF（这是其表示能力的下界）；（ii）曲率感知调度在深层为正曲率（外围）节点保留的判别信息严格多于统一深度传播。NTCF的性能优于大多数广泛使用的GCF骨干模型，且可集成到现有先进的自监督模型中作为骨干，替换其原始骨干以提升性能。在三个公开数据集上的大量实验证明了NTCF的优越性。
英文摘要
Graph Collaborative Filtering (GCF) has become the dominant paradigm in modern recommender systems by modeling user-item interactions as a bipartite graph and propagating embeddings through a fixed number of message-passing layers. However, applying a uniform propagation depth to every node ignores a fundamental property of real interaction graphs: nodes differ substantially in their local connectivity, so peripheral nodes quickly suffer from over-smoothing while hub-like nodes remain under-explored beyond their immediate neighborhood. In this paper, we revisit GCF from a tree-structured perspective and propose Neural Tree Collaborative Filtering (NTCF), a framework that re-interprets each node's local neighborhood as a rooted tree and assigns a node-specific propagation depth based on a closed-form local-degree-imbalance score that serves as a discrete Ricci-curvature proxy. We provide a theoretical analysis showing that (i) NTCF strictly generalizes NGCF, degenerating to NGCF when all curvature-induced depth adjustments vanish (a lower bound on its representation power), and (ii) the curvature-aware schedule retains strictly more discriminative information at deep layers on positively-curved (peripheral) nodes than uniform-depth propagation. NTCF can achieve higher performance than most widely used GCF backbone models and can be integrated into existing advanced self-supervised models as a backbone, replacing their original backbone to achieve enhanced performance. Extensive experiments on three public datasets demonstrate the superiority of NTCF.
