双分支图蒸馏结合语义先验的鲁棒冷启动推荐
原标题：Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation
AI 摘要
香港科技大学（广州）的研究者提出 DualGCN，一种双分支图知识蒸馏框架，用于解决严格冷启动推荐中因物品拓扑孤立导致的表征崩溃问题。该方法采用解耦的两阶段训练：先构建由拓扑平滑语义先验锚定的协同教师网络，再用语义学生分支通过残差映射和余弦蒸馏目标实现跨模态对齐，并引入动态范数重建机制校准新旧物品的尺度。在三个真实数据集上的实验表明，DualGCN 在严格冷启动场景下显著优于现有基线，同时保持活跃物品的推荐精度。
正文节选
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation Yuanfeng QIU The Hong Kong University of Science and Technology (Guangzhou) Abstract Graph Collaborative Filtering models have achieved remarkable success in recommendation systems. However, they inherently suffer from a catastrophic representation collapse in strict cold-start scenarios due to the topological isolation of unobserved items. While integrating multi-modal seman