Sharpness-Consistent Cross-Domain Recommendation for Cold-Start Items

Cold-start remains a fundamental challenge in recommendation systems due to the scarcity of interaction data. Recent methods address this issue by leveraging semantic ID embeddings and cross-domain transfer techniques, achieving notable progress. However, the common practice of learning semantic ID embeddings and training the recommendation model in separate stages hinders the generalization capability of semantic IDs throughout the training process. In this work, we propose Sharpness-Consistent Cross-Domain Recommendation (SC2 Rec), a novel framework designed to enhance the generalization of semantic ID-based models in cold-start scenarios. SC2 Rec alternately optimizes the sharpness of the loss landscape and enforces landscape consistency between warm and cold domains, leading to unified and flatter minima and improved generalization. Extensive experiments on industrial datasets demonstrate the effectiveness of SC2 Rec. Furthermore, we release a high-quality dataset to facilitate further research in this area.