Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Obviously, these markets have similar goods but different users. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (CMR) has been proposed to enhance data-scarce markets by leveraging auxiliary information from data-rich markets. Previous works fine-tune the pre-trained model on the local market after freezing part of the parameters or introducing inter-market similarity into the local market to improve the performance of CMR. However, they generally do not consider eliminating the mutual interference between markets. Therefore, the existing methods are neither unable to learn unbiased general knowledge nor efficient transfer reusable information across markets. In this paper, we propose a novel attention-based model called Bert4CMR to simultaneously improve all markets' recommendation performance. Specifically, we employ the attention mechanism to capture user interests by modelling user behavioural sequences. We pre-train the proposed model on global data to learn the general knowledge of items. Then we fine-tune specific target markets to perform local recommendations. We propose market embedding to model the bias of each market and reduce the mutual inference between the parallel markets. Extensive experiments conducted on seven markets show that our model is state-of-the-art. Our model outperforms the suboptimal model by 4.82%, 4.73%, 7.66% and 6.49% on average of seven datasets in terms of four metrics, respectively. We conduct ablation experiments to analyse the effectiveness of the proposed components. Experimental results indicate that our model is able to learn general knowledge through global data and shield the mutual interference between markets.

翻译：摘要：现实世界中的跨国电商企业（如亚马逊和eBay）服务于多个国家和地区。显然，这些市场拥有相似的商品但用户群体不同。部分市场数据稀缺，而另一些则数据丰富。近年来，跨市场推荐（CMR）被提出，旨在通过利用数据丰富市场的辅助信息来增强数据稀缺市场。此前的研究通过冻结部分参数在本地市场微调预训练模型，或通过引入市场间相似性来提升CMR性能。然而，这些方法通常未考虑消除市场间的相互干扰。因此，现有方法既无法学习无偏的通用知识，也无法高效跨市场迁移可复用信息。本文提出一种名为Bert4CMR的新型注意力模型，可同时提升所有市场的推荐性能。具体而言，我们采用注意力机制通过建模用户行为序列来捕获用户兴趣。在全局数据上预训练所提模型以学习商品的通用知识，随后对特定目标市场进行微调以实现本地推荐。我们提出市场嵌入（market embedding）来建模各市场偏差，并减少并行市场间的相互干扰。在七个市场上的大量实验表明，本模型达到当前最优水平。在四个评价指标上，本模型在七个数据集上的平均性能分别超过次优模型4.82%、4.73%、7.66%和6.49%。通过消融实验分析各组件有效性，结果表明本模型能通过全局数据学习通用知识，并有效屏蔽市场间的相互干扰。