Cross-Domain Recommendation (CDR) is a promising paradigm inspired by transfer learning to solve the cold-start problem in recommender systems. Existing state-of-the-art CDR methods train an explicit mapping function to transfer the cold-start users from a data-rich source domain to a target domain. However, a limitation of these methods is that the mapping function is trained on overlapping users across domains, while only a small number of overlapping users are available for training. By visualizing the loss landscape of the existing CDR model, we find that training on a small number of overlapping users causes the model to converge to sharp minima, leading to poor generalization. Based on this observation, we leverage loss-geometry-based machine learning approach and propose a novel CDR method called Sharpness-Aware CDR (SCDR). Our proposed method simultaneously optimizes recommendation loss and loss sharpness, leading to better generalization with theoretical guarantees. Empirical studies on real-world datasets demonstrate that SCDR significantly outperforms the other CDR models for cold-start recommendation tasks, while concurrently enhancing the model's robustness to adversarial attacks.

翻译：跨域推荐是一种借鉴迁移学习思想以解决推荐系统中冷启动问题的有前景范式。现有最先进的跨域推荐方法通过训练显式映射函数，将冷启动用户从数据丰富的源域迁移至目标域。然而，这些方法存在一个局限：映射函数仅基于跨域重叠用户进行训练，而实际可用的重叠用户数量往往极少。通过对现有跨域推荐模型的损失景观进行可视化分析，我们发现基于少量重叠用户的训练会导致模型收敛至尖锐极小值，从而引发泛化能力不足的问题。基于此观察，我们采用基于损失几何的机器学习方法，提出了一种名为锐度感知跨域推荐的新型跨域推荐方法。该方法通过同步优化推荐损失与损失锐度，在理论保证下实现了更优的泛化性能。在真实数据集上的实证研究表明，SCDR在冷启动推荐任务中显著优于其他跨域推荐模型，同时增强了模型对抗对抗性攻击的鲁棒性。