通过多维度用户画像提升跨域推荐效果，解决偏好异质性问题。
Multi-TAP: Multi-criteria Target Adaptive Persona Modeling for Cross-Domain Recommendation
- 构建多标准目标自适应人物模型，捕捉用户域内偏好差异。
- 根据目标域动态筛选源域信号，确保知识迁移的相关性。
- 在真实数据集上优于主流方法，适合个性化推荐场景。
跨域推荐（CDR）旨在通过跨域知识迁移缓解数据稀疏问题，但现有方法多依赖粗粒度行为信号，常忽略用户在单个域内的偏好异质性。本文提出 Multi-TAP，一种多准则目标自适应人物建模框架，通过语义人物建模显式捕捉这种异质性。为实现有效迁移，Multi-TAP 根据目标域条件选择性引入源域信号，保证知识迁移过程中的相关性。在真实数据集上的实验表明，Multi-TAP 持续优于当前最优的 CDR 方法，凸显了建模域内异质性对鲁棒跨域推荐的重要性。Multi-TAP 的代码已公开于 https://github.com/archivehee/Multi-TAP。
原文摘要 · Abstract (English)
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.
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