APCL：显式间接关系学习实现自适应偏好建模的个性化时尚匹配
原标题：Adaptive Preference Modeling via Explicit Indirect Relational Learning for Personalized Fashion Matching
AI 摘要
该论文提出 APCL（Adaptive Preference with Contrastive Learning）框架，用于个性化时尚搭配推荐。APCL 通过相关性引导的自适应聚合机制显式构建间接的用户-物品与物品-物品关系，并引入功能视图对比学习策略对齐直接与间接的偏好及兼容性表示，同时融合多模态视觉与文本信息。在两个基准数据集上的实验表明，APCL 持续优于代表性基线方法，尤其在冷启动场景中表现突出。
正文节选
Adaptive Preference Modeling via Explicit Indirect Relational Learning for Personalized Fashion Matching Abstract Personalized fashion complementary recommendation requires jointly modeling user preferences and item compatibility under sparse and multimodal data conditions. Existing approaches often capture higher-order relational signals implicitly through graph propagation or rely on direct interaction data, limiting their ability to explicitly model indirect preference and compatibility relat