Modeling anisotropic preference manifolds for robust graph-based fashion recommendation.

Graph Convolutional Networks (GNNs) have become a dominant paradigm for collaborative filtering; however, their efficacy in complex domains like fashion is often hindered by a fundamental geometric mismatch between model assumptions and data reality. Conventional methods typically rely on scalar aggregation and Euclidean metrics, which implicitly assume that user interest clusters are isotropic (spherical) and uniformly dense. This assumption fails to capture the complexity of fashion preferences, where style distributions exhibit significant anisotropic variance-ranging from sparse, broad categories to dense, niche trends. To bridge this gap, we propose the Multi-Interest Mahalanobis Denoising Graph Convolutional Network (MIMD-GCN), a framework that synergizes structural disentanglement with geometry-aware denoising. We introduce a Poly-Attention mechanism to disentangle user representations into multiple latent interest centers, thereby resolving the collapse of diverse preferences into single vectors. Furthermore, we construct an anisotropic denoising module based on a learnable Mahalanobis distance barrier. Unlike static Euclidean thresholds, this mechanism dynamically adapts to the covariance structure of specific interest manifolds, establishing elliptical boundaries that effectively isolate spurious interactions while preserving valid niche signals. Extensive experiments on Amazon-Clothing and Taobao datasets demonstrate that MIMD-GCN consistently improves recommendation performance over strong baselines and exhibits enhanced robustness under synthetic noise. These results suggest that anisotropic modeling provides a beneficial geometric perspective for capturing complex user preferences in fashion recommendation.