Cross-Domain Recommendation has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. In this paper, we focus on the Cross-Domain Cold-Start Recommendation ( CDCSR ) problem. That is, how to leverage the information from a source domain, where items are ’warm’, to improve the recommendation performance of a target domain, where items are ’cold’. It has two main challenges, i.e., (1) how to efficiently reduce the discrepancy between the latent embedding distribution across domains and (2) how to generate more robust and stable cold item embeddings. To address these two challenges, we propose CPKSPA , a cross-domain recommendation framework for the CDCSR problem. CPKSPA contains three modules, i.e., rating prediction module, embedding distribution alignment module, and contrastive augmentation module. To start with, we first utilize the rating prediction module to model user-item interactions. To solve the first challenge, we propose proxy Stein path alignment with typical-subgroup discovering algorithm in the embedding distribution alignment module. To tackle the second challenge, we propose the contrastive augmentation module which adopts contrastive augmentation learning to generate more stable and robust cold item embeddings. Our empirical study on Douban and Amazon datasets demonstrates that CPKSPA significantly outperforms the state-of-the-art models.

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