Cross-domain recommendation (CDR) methods are proposed to tackle the sparsity problem in click through rate (CTR) estimation. Existing CDR methods directly transfer knowledge from the source domains to the target domain and ignore the heterogeneities among domains, including feature dimensional heterogeneity and latent space heterogeneity, which may lead to negative transfer. Besides, most of the existing methods are based on single-source transfer, which cannot simultaneously utilize knowledge from multiple source domains to further improve the model performance in the target domain. In this paper, we propose a centralized-distributed transfer model (CDTM) for CDR based on multi-source heterogeneous transfer learning. To address the issue of feature dimension heterogeneity, we build a dual embedding structure: domain specific embedding (DSE) and global shared embedding (GSE) to model the feature representation in the single domain and the commonalities in the global space,separately. To solve the latent space heterogeneity, the transfer matrix and attention mechanism are used to map and combine DSE and GSE adaptively. Extensive offline and online experiments demonstrate the effectiveness of our model.

翻译：跨域推荐方法旨在解决点击率估计中的稀疏性问题。现有跨域推荐方法直接将知识从源域迁移至目标域，忽略了域间异质性（包括特征维度异质性与潜在空间异质性），可能导致负迁移。此外，现有方法多基于单源迁移，无法同时利用多个源域知识以进一步提升目标域模型性能。本文提出一种基于多源异构迁移学习的集中式-分布式迁移模型。针对特征维度异质性问题，构建双嵌入结构：域特定嵌入与全局共享嵌入，分别建模单域特征表示与全局空间共性。为解决潜在空间异质性，采用迁移矩阵与注意力机制自适应地映射并融合域特定嵌入与全局共享嵌入。大量离线与在线实验验证了模型的有效性。