Popularity bias is a common challenge in recommender systems. It often causes unbalanced item recommendation performance and intensifies the Matthew effect. Due to limited user-item interactions, unpopular items are frequently constrained to the embedding neighborhoods of only a few users, leading to representation collapse and weakening the model's generalization. Although existing supervised alignment and reweighting methods can help mitigate this problem, they still face two major limitations: (1) they overlook the inherent variability among different Graph Convolutional Networks (GCNs) layers, which can result in negative gains in deeper layers; (2) they rely heavily on fixed hyperparameters to balance popular and unpopular items, limiting adaptability to diverse data distributions and increasing model complexity. To address these challenges, we propose Graph-Structured Dual Adaptation Framework (GSDA), a dual adaptive framework for mitigating popularity bias in recommendation. Our theoretical analysis shows that supervised alignment in GCNs is hindered by the over-smoothing effect, where the distinction between popular and unpopular items diminishes as layers deepen, reducing the effectiveness of alignment at deeper levels. To overcome this limitation, GSDA integrates a hierarchical adaptive alignment mechanism that counteracts entropy decay across layers together with a distribution-aware contrastive weighting strategy based on the Gini coefficient, enabling the model to adapt its debiasing strength dynamically without relying on fixed hyperparameters. Extensive experiments on three benchmark datasets demonstrate that GSDA effectively alleviates popularity bias while consistently outperforming state-of-the-art methods in recommendation performance.

翻译：流行度偏差是推荐系统中常见的挑战。它通常导致物品推荐性能失衡并加剧马太效应。由于用户-物品交互有限，非流行物品常被限制在少数用户的嵌入邻域内，导致表示坍缩并削弱模型的泛化能力。尽管现有的监督对齐和重加权方法有助于缓解此问题，但仍面临两大局限：(1) 忽略了不同图卷积网络(GCNs)层间的固有差异性，可能导致深层网络出现负增益；(2) 严重依赖固定超参数来平衡流行与非流行物品，限制了模型对不同数据分布的适应性并增加了复杂度。为应对这些挑战，我们提出图结构双适应框架(GSDA)——一种缓解推荐系统流行度偏差的双自适应框架。理论分析表明，GCNs中的监督对齐受到过度平滑效应的阻碍：随着网络层数加深，流行与非流行物品的区分度逐渐减弱，降低了深层对齐的有效性。为突破此限制，GSDA整合了层级自适应对齐机制（通过抵消层间熵衰减）与基于基尼系数的分布感知对比加权策略，使模型能够动态调整去偏强度而无需依赖固定超参数。在三个基准数据集上的大量实验表明，GSDA在有效缓解流行度偏差的同时，其推荐性能持续优于现有最先进方法。