Multi-TAP: Multi-criteria Target Adaptive Persona Modeling for Cross-Domain Recommendation
This work provides a more robust cross-domain recommendation system for users by better capturing their diverse preferences within a domain, which is an incremental improvement over existing methods.
This paper addresses data sparsity in cross-domain recommendation by modeling intra-domain user preference heterogeneity through semantic personas. The proposed Multi-TAP framework selectively transfers knowledge from source domains, outperforming state-of-the-art methods on real-world datasets.
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.