Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain. However, through empirical studies, we uncover a critical bias inherent in these approaches: while overlapping users experience significant enhancements in recommendation quality, non-overlapping users benefit minimally and even face performance degradation. This unfairness may erode user trust, and, consequently, negatively impact business engagement and revenue. To address this issue, we propose a novel solution that generates virtual source-domain users for non-overlapping target-domain users. Our method utilizes a dual attention mechanism to discern similarities between overlapping and non-overlapping users, thereby synthesizing realistic virtual user embeddings. We further introduce a limiter component that ensures the generated virtual users align with real-data distributions while preserving each user's unique characteristics. Notably, our method is model-agnostic and can be seamlessly integrated into any CDR model. Comprehensive experiments conducted on three public datasets with five CDR baselines demonstrate that our method effectively mitigates the CDR non-overlapping user bias, without loss of overall accuracy. Our code is publicly available at https://github.com/WeixinChen98/VUG.

翻译：跨域推荐方法主要利用重叠用户将知识从源域迁移到目标域。然而，通过实证研究，我们发现这些方法存在一个关键偏差：虽然重叠用户的推荐质量得到显著提升，但非重叠用户获益甚微，甚至面临性能下降。这种不公平性可能损害用户信任，进而对商业参与度和收入产生负面影响。为解决此问题，我们提出一种新颖方案，为非重叠的目标域用户生成虚拟源域用户。我们的方法采用双重注意力机制来识别重叠用户与非重叠用户之间的相似性，从而合成真实的虚拟用户嵌入。我们进一步引入限制器组件，确保生成的虚拟用户符合真实数据分布，同时保留每个用户的独特特征。值得注意的是，我们的方法具有模型无关性，可无缝集成到任何跨域推荐模型中。在三个公开数据集上使用五种跨域推荐基线模型进行的全面实验表明，我们的方法能有效缓解跨域推荐中的非重叠用户偏差，且不损失整体准确性。代码已公开于 https://github.com/WeixinChen98/VUG。