To solve the problem of data sparsity and cold start, the cross-domain recommendation is a promising research direction in the recommender system. The goal of cross-domain recommendation is to transfer learned knowledge from the source domain to the target domain by different means to improve the performance of the recommendation. But most approaches face the distribution misalignment. In this paper, we propose a joint learning cross-domain recommendation model that can extract domain-specific and common features simultaneously, and only use the implicit feedback data of users without additional auxiliary information. To the best of our knowledge, it is the first attempt to combine the sliced Wasserstein distance and canonical correlation analysis for the cross-domain recommendation scenario. Our one intuition is to reduce the reconstruction error caused by the variational inference based autoencoder model by the optimal transportation theory. Another attempt is to improve the correlation between domains by combining the idea of the canonical correlation analysis. With rigorous experiments, we empirically demonstrated that our model can achieve better performance compared with the state-of-the-art methods.

为了解决数据稀疏性和冷启动问题，跨域推荐是推荐系统中一个很有前景的研究方向。跨域推荐的目标是通过不同的方式将学习到的知识从源域迁移到目标域，以提高推荐的性能。但大多数方法都面临分布错位。在本文中，我们提出了一种联合学习跨域推荐模型，该模型可以同时提取特定领域和公共特征，并且仅使用用户的隐式反馈数据，而无需额外的辅助信息。据我们所知，这是首次尝试将切片的 Wasserstein 距离和典型相关分析结合起来用于跨域推荐场景。我们的一个直觉是通过最优运输理论减少基于变分推理的自动编码器模型引起的重构误差。另一种尝试是通过结合典型相关分析的思想来提高域之间的相关性。通过严格的实验，我们凭经验证明，与最先进的方法相比，我们的模型可以实现更好的性能。