arXiv：cs.LG· Yuanfeng Qiu·· 6 小时前
DualGCN：面向冷启动推荐的双分支图知识蒸馏框架
Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation
AI 导读
针对严格冷启动场景下未观测物品因拓扑孤立导致的表征坍塌问题，研究者提出双分支图知识蒸馏框架 DualGCN。该方法先用拓扑平滑语义先验锚定协同教师网络，再通过残差映射与幅度无关的余弦蒸馏目标完成跨模态对齐，并用动态范数重建校准物品尺度。在三个真实基准数据集上，DualGCN 在严格冷启动场景显著优于现有最优基线，同时保持活跃物品的推荐准确率。
正文
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 semantic features offers a potential remedy, directly mapping isolated semantic attributes into a globally topology-driven collaborative space frequently precipitates representation degradation and optimization conflicts. To bridge this fundamental structural divide, we propose DualGCN, an innovative Dual-Branch Graph Knowledge Distillation Framework. Operating under a decoupled two-stage training paradigm, DualGCN first constructs a robust collaborative teacher network where item representations are anchored by a topology-smoothed semantic prior, effectively reshaping the latent manifold. Subsequently, a semantic student branch employs a residual mapping mechanism and a magnitude-agnostic cosine distillation objective to achieve high-fidelity cross-modal alignment without disrupting established collaborative signals. Furthermore, we introduce a dynamic norm reconstruction mechanism to rigorously calibrate the physical scales of novel and warm items during global retrieval. Extensive experiments across three real-world benchmark datasets demonstrate that DualGCN significantly outperforms state-of-the-art baselines in strict cold-start scenarios while comprehensively preserving recommendation accuracy for highly active items.
| Subjects: | Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19578 [cs.IR] |
| (or arXiv:2610.19578v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19578 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuanfeng Qiu [view email]
[v1]
Wed, 14 Oct 2026 04:43:38 UTC (162 KB)
来源：arXiv：cs.LG · arxiv.org