Discovering user preferences across different domains is pivotal in cross-domain recommendation systems, particularly when platforms lack comprehensive user-item interactive data. The limited presence of shared users often hampers the effective modeling of common preferences. While leveraging shared items' attributes, such as category and popularity, can enhance cross-domain recommendation performance, the scarcity of shared items between domains has limited research in this area. To address this, we propose a Coherence-guided Preference Disentanglement (CoPD) method aimed at improving cross-domain recommendation by i) explicitly extracting shared item attributes to guide the learning of shared user preferences and ii) disentangling these preferences to identify specific user interests transferred between domains. CoPD introduces coherence constraints on item embeddings of shared and specific domains, aiding in extracting shared attributes. Moreover, it utilizes these attributes to guide the disentanglement of user preferences into separate embeddings for interest and conformity through a popularity-weighted loss. Experiments conducted on real-world datasets demonstrate the superior performance of our proposed CoPD over existing competitive baselines, highlighting its effectiveness in enhancing cross-domain recommendation performance.

翻译：在跨域推荐系统中，发现用户在不同领域的偏好至关重要，尤其是在平台缺乏全面的用户-物品交互数据时。共享用户的有限存在常常阻碍了共同偏好的有效建模。虽然利用共享物品的属性（如类别和流行度）可以提升跨域推荐性能，但领域间共享物品的稀缺性限制了该方向的研究。为解决这一问题，我们提出了一种基于一致性引导的偏好解耦方法，旨在通过以下方式改进跨域推荐：i) 显式提取共享物品属性以指导共享用户偏好的学习；ii) 解耦这些偏好以识别领域间迁移的特定用户兴趣。该方法对共享域和特定域的物品嵌入施加一致性约束，以辅助提取共享属性。此外，它利用这些属性，通过流行度加权损失引导将用户偏好解耦为兴趣和从众性的独立嵌入。在真实数据集上进行的实验表明，我们提出的方法优于现有竞争基线，突显了其在提升跨域推荐性能方面的有效性。