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.

Our preferences are more fluid than most recommendation systems assume. The qualities that matter when choosing one product may carry far less weight when choosing another, even for the same person.

A research team, led by Professor Yeon-Chang Lee of the Graduate School of Artificial Intelligence at UNIST has developed Multi-TAP, an AI framework that recognizes these differences across product categories and uses them to make more personalized recommendations.

Recommendation systems often have plenty of information about a user in one area but relatively little in another. Cross-domain recommendation tries to fill that gap by drawing on a user's activity elsewhere. But existing methods can miss an important distinction: preferences that matter in one product category may not carry over neatly to another.

Multi-TAP accounts for these differences by building multiple semantic personas from a user's purchase history and product information. The system looks at factors such as price, ratings, review counts, purchasing frequency, and the range of products a person buys, allowing it to distinguish how those priorities change across categories.

A large language model (LLM) helps turn these patterns into representations that retain those category-specific differences. Multi-TAP then combines them with purchasing behavior learned by the recommendation model, while determining how much information from one domain is actually useful in another. This allows the system to draw selectively on past behavior rather than treating everything known about a user as equally relevant.

The researchers tested Multi-TAP using Amazon purchase data across electronics, home and household products, sporting goods, clothing, and toys. It achieved the best performance in five of six cross-domain recommendation tasks. In its strongest result, the rate at which a product a user actually purchased appeared among the top five recommendations was up to 36.3% higher than with the previous best-performing method.

“Consumer preferences can vary depending on the context and the type of product,” said Professor Lee. “Multi-TAP represents these differences through multiple personas and uses information from other domains according to its relevance. This could help recommendation systems better reflect individual preferences, particularly in areas where a user has little purchase history.”

Daehee Kang served as first author of the study. Their work was accepted to the 2026 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), held in Jeju Island, South Korea from August 9-13, 2026. The research was supported by the Ministry of Science and ICT (MSIT), the Institute of Information & Communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea (NRF).

Journal Reference

Daehee Kang and Yeon-Chang Lee, “Multi-TAP: Multi-criteria Target Adaptive Persona Modeling for Cross-Domain Recommendation,”   ACM KDD' 26  (2026).