从狄利克雷能量视角揭示图消息传递如何放大流行度偏差，提出新方法提升长尾推荐
How Does Topology Bias Distort Message Passing? A Dirichlet Energy Perspective
- 用狄利克雷能量分析图消息传递机制，发现其天然放大连接度高的节点
- 提出测试时的单纯形传播（TSP），在高阶结构中降低拓扑偏差
- 在五个真实数据集上显著提升长尾物品推荐效果，适合推荐系统公平性研究
基于图的推荐系统通过建模用户与物品间的高阶交互取得了显著成效，但其性能受流行度偏差严重影响，导致交互图结构失真，即拓扑偏差。这使热门物品被过度代表，通过用户-系统反馈环路强化偏见与公平性问题。尽管已有研究关注嵌入或梯度层面的偏差，却忽视了拓扑偏差对消息传递过程的根本性扭曲。本文从狄利克雷能量视角提供理论与实证分析，揭示图消息传递会固有地放大拓扑偏差，并持续有利于高度连接的节点。为应对该问题，我们提出测试时单纯形传播（TSP），将消息传递扩展至更高阶单纯形复形，通过引入超越成对连接的丰富结构，有效缓解有害的拓扑偏差，在推理阶段显著提升长尾物品的表征与推荐表现。在五个真实世界数据集上的大量实验表明，该方法在缓解拓扑偏差和提升推荐质量方面具有显著优势。
原文摘要 · Abstract (English)
Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly undermined by popularity bias, which distorts the interaction graph's structure, referred to as topology bias. This leads to overrepresentation of popular items, thereby reinforcing biases and fairness issues through the user-system feedback loop. Despite attempts to study this effect, most prior work focuses on the embedding or gradient level bias, overlooking how topology bias fundamentally distorts the message passing process itself. We bridge this gap by providing an empirical and theoretical analysis from a Dirichlet energy perspective, revealing that graph message passing inherently amplifies topology bias and consistently benefits highly connected nodes. To address these limitations, we propose Test-time Simplicial Propagation (TSP), which extends message passing to higher-order simplicial complexes. By incorporating richer structures beyond pairwise connections, TSP mitigates harmful topology bias and substantially improves the representation and recommendation of long-tail items during inference. Extensive experiments across five real-world datasets demonstrate the superiority of our approach in mitigating topology bias and enhancing recommendation quality.
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