The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target), has received notable attention. Nevertheless, the majority of existing methodologies assume a Euclidean embedding space, encountering difficulties in accurately representing richer text information and managing complex interactions between users and items. This paper advocates a hyperbolic CDR approach based on review texts for modeling user-item relationships. We first emphasize that conventional distance-based domain alignment techniques may cause problems because small modifications in hyperbolic geometry result in magnified perturbations, ultimately leading to the collapse of hierarchical structures. To address this challenge, we propose hierarchy-aware embedding and domain alignment schemes that adjust the scale to extract domain-shareable information without disrupting structural forms. The process involves the initial embedding of review texts in hyperbolic space, followed by feature extraction incorporating degree-based normalization and structure alignment. We conducted extensive experiments to substantiate the efficiency, robustness, and scalability of our proposed model in comparison to state-of-the-art baselines.

翻译：数据稀疏性问题对推荐系统构成了重大挑战。为应对此问题，已有研究提出利用评论文本等辅助信息的算法。此外，跨域推荐通过捕捉可跨域共享的知识并将其从数据更丰富的源域迁移至更稀疏的目标域，已获得显著关注。然而，现有方法大多假设欧几里得嵌入空间，难以准确表征丰富的文本信息并处理用户与物品间的复杂交互。本文提出一种基于评论文本的双曲跨域推荐方法以建模用户-物品关系。我们首先指出，传统的基于距离的域对齐技术可能引发问题，因为双曲几何中的微小修改会导致扰动放大，最终造成层次结构崩塌。为解决这一挑战，我们提出层次感知嵌入与域对齐方案，通过尺度调整来提取可跨域共享信息而不破坏结构形态。该方法首先将评论文本嵌入双曲空间，随后结合基于度数的归一化与结构对齐进行特征提取。通过大量实验验证，相较于前沿基线模型，我们提出的模型在效率、鲁棒性和可扩展性方面均表现出显著优势。