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

2026-03-07

AI Summary

Existing cross-domain recommendation methods rely on coarse-grained behavioral signals and overlook the heterogeneity of user preferences within the target domain, thereby limiting the effectiveness of knowledge transfer. To address this limitation, this work proposes Multi-TAP, a novel framework that introduces, for the first time, a multi-criteria target-adaptive persona modeling mechanism. This mechanism explicitly captures intra-domain preference heterogeneity through semantically meaningful personas and incorporates a conditional source-domain signal selection strategy to enable adaptive cross-domain knowledge fusion. Extensive experiments on multiple real-world datasets demonstrate that Multi-TAP significantly outperforms state-of-the-art baselines, validating that explicitly modeling intra-domain heterogeneity enhances both the accuracy and robustness of cross-domain recommendations.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

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.

Problem

• cross-domain recommendation
• data sparsity
• intra-domain heterogeneity
• user preferences
• knowledge transfer

Innovation

• cross-domain recommendation
• persona modeling
• intra-domain heterogeneity
• target-adaptive transfer
• semantic user representation