PCGNet：统一共享与特定信息用于时尚搭配推荐

中文摘要 AI 辅助

在时尚领域，推荐与所选单品相搭配的互补服装是一项关键的交叉销售技术，能够提升客户满意度。然而，时尚搭配面临重大挑战，因为推荐不仅需要符合用户个人的时尚偏好，还必须确保服装之间的兼容性。这些挑战体现在两个方面。首先，现有模型通常假设产品兼容性与个性化用户偏好之间存在过于简化的解耦关系，忽视了二者之间自然的复杂性。其次，现有的数据驱动方法并未针对现实世界的时尚数据进行优化，这些数据通常稀疏且以噪声交互为特征。为解决这些挑战，我们提出了个性化兼容性图网络（Personalized Compatibility Graph Network），这是一个多目标图学习框架，有机地统一了产品兼容性与个人偏好的建模。PCGNet利用对比互信息最大化来提取并对齐共享模式与视图特定模式，从而捕捉兼容性与个人偏好之间的复杂交互。此外，我们引入了相关性感知的邻居采样和可学习的全局图增强，通过直接从图中挖掘的自监督信号来增强模型，确保表征更加稳定且信息丰富。最后，PCGNet通过联合优化BPR排序损失和多视图互信息损失来生成推荐分数。在两个基准数据集上的实验验证表明，PCGNet在所有四个评估指标上显著优于最先进的方法。

英文摘要

In fashion domain, recommending complementary clothing items that match selected pieces is a crucial cross-selling technique that improves customer satisfaction. Nevertheless, fashion matching presents significant challenges, as recommendations must not only align with individual user fashion preferences but also ensure compatibility between garments. These challenges are twofold. First, existing models often assume an overly simplified decoupled relationship between product compatibility and personalized user preferences, overlooking the natural complexity between the two. Second, existing data-driven approaches are not optimized for real-world fashion data, which is typically sparse and characterized by noisy interactions. To address these challenges, we propose Personalized Compatibility Graph Network, a multi-objective graph learning framework that organically unifies the modeling of product compatibility and personal preferences. PCGNet uses contrastive mutual information maximization to extract and align shared and view-specific patterns, thereby capturing the complex interplay between compatibility and personal preferences. Moreover, we introduce a correlation-aware neighbor sampling and a learnable global graph augmentation, which enhance the model by incorporating self-supervised signals mined directly from the graph, ensuring more stable and informative representations. Finally, PCGNet generates recommendation scores through the joint optimization of BPR ranking loss and multi-view mutual information losses. Experimental validation on two benchmark datasets demonstrates that PCGNet significantly outperforming state-of-the-art methods across all four evaluation metrics.

发表机构

• The Hong Kong University of Science and Technology（香港科技大学）

机构由 AI 辅助整理，请以论文原文为准。