Multi-TAP: Multi-criteria Target Adaptive Persona Modeling for Cross-Domain Recommendation

TL;DR

Multi-TAP introduces a novel multi-criteria persona modeling framework for cross-domain recommendation, explicitly capturing user preference heterogeneity and selectively transferring relevant knowledge to improve recommendation accuracy.

Contribution

It presents a new semantic persona modeling approach that accounts for intra-domain heterogeneity and enhances knowledge transfer in cross-domain recommendation systems.

Findings

Outperforms state-of-the-art CDR methods on real-world datasets

Effectively captures intra-domain user preference heterogeneity

Demonstrates robustness through targeted knowledge transfer

Abstract

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.