自适应偏好建模：通过显式间接关系学习实现个性化时尚搭配
Adaptive Preference Modeling via Explicit Indirect Relational Learning for Personalized Fashion Matching
- 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。
AI总结：
针对个性化时尚搭配中稀疏数据下间接关系建模不足的问题，提出APCL框架，通过显式间接关系建模与对比学习，在多模态数据上提升推荐性能，实验验证其优于基线方法。
AI中文摘要：
个性化时尚互补推荐需要在稀疏和多模态数据条件下联合建模用户偏好和物品兼容性。现有方法通常通过图传播隐式捕获高阶关系信号，或依赖直接交互数据，这限制了它们显式建模间接偏好和兼容性关系的能力。为解决这一局限，我们提出了一种自适应偏好与对比学习框架（APCL），该框架在统一的推荐架构中显式建模直接和间接关系信号。具体而言，APCL通过相关性引导的自适应聚合机制构建间接用户-物品和物品-物品关系，并将其表示为专门的个性化和兼容性视图。为改进表示学习，我们进一步引入了一种功能视图对比学习策略，该策略对齐直接和间接偏好表示以及直接和间接兼容性表示，鼓励跨关系上下文的一致性。通过将多模态视觉和文本信息与显式间接关系建模相结合，APCL捕获了更丰富的语义特征，同时提高了稀疏交互设置下的鲁棒性。在两个基准时尚推荐数据集上的实验表明，APCL持续优于代表性基线方法。
英文摘要：
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 relationships. To address this limitation, we propose an Adaptive Preference with Contrastive Learning framework (APCL) that explicitly models both direct and indirect relational signals within a unified recommendation architecture. Specifically, APCL constructs indirect user-item and item-item relationships through a correlation-guided adaptive aggregation mechanism and represents them as dedicated personalization and compatibility views. To improve representation learning, we further introduce a functional view contrastive learning strategy that aligns direct and indirect preference representations and direct and indirect compatibility representations, encouraging consistency across relational contexts. By integrating multimodal visual and textual information with explicit indirect relational modeling, APCL captures richer semantic characteristics while improving robustness in sparse-interaction settings. Experiments on two benchmark fashion recommendation datasets demonstrate that APCL consistently outperforms representative baseline methods.
