Cross-domain recommendation (CDR) aims to alleviate data sparsity by transferring knowledge across domains, yet existing methods primarily rely on coarse-grained behavioral signals and often overlook intra-domain heterogeneity in user preferences. We propose Multi-TAP, a multi-criteria target-adaptive persona framework that explicitly captures such heterogeneity through semantic persona modeling. To enable effective transfer, Multi-TAP selectively incorporates source-domain signals conditioned on the target domain, preserving relevance during knowledge transfer. Experiments on real-world datasets demonstrate that Multi-TAP consistently outperforms state-of-the-art CDR methods, highlighting the importance of modeling intra-domain heterogeneity for robust cross-domain recommendation. The codebase of Multi-TAP is currently available at https://github.com/archivehee/Multi-TAP.

翻译：跨领域推荐旨在通过跨领域知识迁移缓解数据稀疏性问题，然而现有方法主要依赖粗粒度的行为信号，且常忽略用户偏好的领域内异质性。本文提出Multi-TAP——一种多准则目标自适应角色框架，该框架通过语义角色建模显式捕捉此类异质性。为实现有效迁移，Multi-TAP根据目标领域条件选择性地整合源领域信号，在知识迁移过程中保持关联性。在真实数据集上的实验表明，Multi-TAP持续优于最先进的跨领域推荐方法，凸显了领域内异质性建模对构建鲁棒跨领域推荐系统的重要性。Multi-TAP的代码库已发布于https://github.com/archivehee/Multi-TAP。