Popularity bias challenges recommender systems by causing uneven recommendation performance and amplifying the Matthew effect. Limited user-item interactions confine unpopular items within embedding neighborhoods of few users, leading to representation collapse and reduced model generalization. Existing supervised alignment and reweighting methods mitigate this bias but have key limitations: (1) ignoring inherent variability across Graph Convolutional Networks (GCNs) layers, causing negative effects in deeper layers; (2) reliance on fixed hyperparameters to balance item popularity, restricting adaptability and increasing complexity. To address these issues, we propose the Graph-Structured Dual Adaptation Framework (GSDA). Our theoretical analysis identifies a crucial limitation of supervised alignment methods caused by over-smoothing in GCNs. As GCN layers deepen, popular and unpopular items increasingly lose distinctiveness, quantified by reduced conditional entropy. This diminished distinctiveness weakens supervised alignment effectiveness in mitigating popularity bias. Motivated by this, GSDA captures structural and distribution characteristics from the adjacency matrix through a dual adaptive strategy. First, a hierarchical adaptive alignment mechanism uses the adjacency matrix's Frobenius norm for layer-specific weight decay, countering conditional entropy reduction effects at deeper layers. Second, a distribution-aware dynamic contrast weighting strategy, guided by a real-time Gini coefficient, removes dependence on fixed hyperparameters, enabling adaptability to diverse data. Experiments on three benchmark datasets demonstrate GSDA significantly alleviates popularity bias and consistently outperforms state-of-the-art recommendation methods.

翻译：流行度偏差通过导致推荐性能不均和放大马太效应，对推荐系统构成挑战。有限的用户-物品交互将冷门物品限制在少数用户的嵌入邻域内，引发表示坍缩并降低模型泛化能力。现有监督对齐与重加权方法虽能缓解此偏差，但存在关键局限：(1)忽略图卷积网络各层固有的变异性，导致深层网络产生负面效应；(2)依赖固定超参数平衡物品流行度，限制适应性并增加复杂度。为解决这些问题，我们提出图结构双适应框架。理论分析揭示了监督对齐方法因GCN过平滑效应而产生的关键局限：随着GCN层数加深，热门与冷门物品的区分度持续降低，其条件熵减小可量化该现象。区分度减弱削弱了监督对齐缓解流行度偏差的有效性。基于此，GSDA通过双适应策略从邻接矩阵捕获结构与分布特征：首先，分层自适应对齐机制利用邻接矩阵的Frobenius范数实现层特异性权重衰减，以抵消深层条件熵降低效应；其次，分布感知的动态对比加权策略由实时基尼系数指导，消除对固定超参数的依赖，实现对多样化数据的自适应。在三个基准数据集上的实验表明，GSDA能显著缓解流行度偏差，并持续优于当前最先进的推荐方法。