  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.
