AesRec：一个用于美学对齐的服装搭配推荐数据集
AesRec: A Dataset for Aesthetics-Aligned Clothing Outfit Recommendation
AI总结：
AesRec数据集通过系统化的定量美学注释，为开发与美学对齐的服装推荐系统提供了支持，实验表明整合量化美学信息能提升用户审美指导与个性化需求的满足。
摘要：
服装推荐不仅限于生成个性化的装扮；它还是一种重要的审美指导媒介。然而，现有方法主要依赖于用户-物品-装扮交互行为，而忽视了服装美学的显式表示。为了弥合这一差距，我们提出了AesRec基准数据集，该数据集包含系统化的定量美学注释，从而能够开发出与美学对齐的推荐系统。基于专业服装质量标准和时尚审美原则，我们定义了一组多维指标。在物品层面，六个维度被独立评估：轮廓、色度、材料性、工艺、可穿性以及物品级印象。过渡到装扮层面，评估保留了前五个核心属性，并引入了风格协同、视觉和谐以及装扮级印象作为不同的度量标准，以捕捉集体的审美影响。鉴于视觉-语言模型在多模态理解和交互方面的人类般的 proficiency 增加，我们利用它们进行大规模的美学评分。我们在时尚数据集上进行了严格的人机一致性验证，确认了生成评分的可靠性。基于AesRec的实验结果进一步表明，将量化美学信息整合到服装推荐模型中，可以为用户提供审美指导，同时满足其个性化需求。
英文摘要：
Clothing recommendation extends beyond merely generating personalized outfits; it serves as a crucial medium for aesthetic guidance. However, existing methods predominantly rely on user-item-outfit interaction behaviors while overlooking explicit representations of clothing aesthetics. To bridge this gap, we present the AesRec benchmark dataset featuring systematic quantitative aesthetic annotations, thereby enabling the development of aesthetics-aligned recommendation systems. Grounded in professional apparel quality standards and fashion aesthetic principles, we define a multidimensional set of indicators. At the item level, six dimensions are independently assessed: silhouette, chromaticity, materiality, craftsmanship, wearability, and item-level impression. Transitioning to the outfit level, the evaluation retains the first five core attributes while introducing stylistic synergy, visual harmony, and outfit-level impression as distinct metrics to capture the collective aesthetic impact. Given the increasing human-like proficiency of Vision-Language Models in multimodal understanding and interaction, we leverage them for large-scale aesthetic scoring. We conduct rigorous human-machine consistency validation on a fashion dataset, confirming the reliability of the generated ratings. Experimental results based on AesRec further demonstrate that integrating quantified aesthetic information into clothing recommendation models can provide aesthetic guidance for users while fulfilling their personalized requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
