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。为此，研究人员提出了利用评论文本等辅助信息的算法。此外，跨域推荐（CDR）通过捕获域间共享知识并将其从数据丰富的源域迁移至稀疏的目标域，已获得显著关注。然而，现有方法大多基于欧几里得嵌入空间，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CDR方法以建模用户-物品关系。我们首先指出，传统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，最终破坏层次结构。为应对这一挑战，我们提出层次感知嵌入与域对齐方案，通过调整尺度提取域间共享信息的同时不破坏结构形态。该过程首先在双曲空间中对评论文本进行初始嵌入，继而结合基于度数的归一化与结构对齐进行特征提取。我们开展了大量实验，验证了所提模型相较于现有最优基线方法的效率、鲁棒性与可扩展性。