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PPA++: Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain Recommendation
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Yuxi Zhang6, Ji Zhang6, Feiyang Xu5, Lvying Chen6, Bohan Li1, Ning Wang6, Huawei Tu2, Lei Guo4, Hongzhi Yin3
Data Science and Engineering | 2026, 11(1) : 213 - 229
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Data Science and Engineering | 2026, 11(1): 213-229
• RESEARCH PAPERS •
PPA++: Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain Recommendation
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Yuxi Zhang6, Ji Zhang6, Feiyang Xu5, Lvying Chen6, Bohan Li1, Ning Wang6, Huawei Tu2, Lei Guo4, Hongzhi Yin3
Affiliations
  • 1Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Key Laboratory of Intelligent Decision and Digital Operations, Ministry of Industrial and Information Technology, College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China
  • 2Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
  • 3The School of Information Technology & Electric Engineering, The University of Queensland, Brisbane, Australia
  • 4College of Computer Science and Technology, Shandong Normal University, Jinan, China
  • 5Polytechnic Institute, Zhejiang University, Hangzhou, China
  • 6College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    • Yuxi Zhang　

    Feiyang Xu　

    Lvying Chen　

    Huawei Tu　

    Lei Guo　

    Hongzhi Yin　

Published: 2026-03-01 doi: 10.1007/s41019-025-00322-w
Outline
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While user preferences are important to cross-domain recommendation (CDR), existing methods primarily discover preferences under specific, yet possibly redundant, item features. To this end, we first propose a novel Preference Prototype-Aware (PPA) learning method to quantitatively learn user preferences while minimizing disturbances from the source domain. It introduces a mix-encoder and a proto-decoder. On the one hand, the mix-encoder learns better general representations of interacted items and captures the intrinsic relationships between items across different domains. On the other hand, the proto-decoder implements a learnable prototype matching mechanism to quantitatively perceive user preferences, avoiding disturbances caused by item features from the source domain. Moreover, through experiments on PPA, we observe another two issues that affect existing CDR methods' performance, i.e., the semantic deficiency caused by sparse item categories and the imbalance weights caused by different user-item distributions. Thus, we further propose a LoRA-based extractor and a domain cross-attention module to alleviate the two issues, respectively. The PPA incorporating with new extractor and attention module is called PPA++. Extensive experiments show that PPA++ outperforms the other state-of-the-art counterparts in four different CDR scenarios.

Cross-Domain Recommendation  /  Prototype Learning  /  Item Similarity Mining  /  LLM
Yuxi Zhang, Ji Zhang, Feiyang Xu, Lvying Chen, Bohan Li, Ning Wang, Huawei Tu, Lei Guo, Hongzhi Yin. PPA++: Preference Prototype-Aware Learning with Large Language Model for Universal Cross-Domain Recommendation[J]. Data Science and Engineering, 2026 , 11 (1) : 213 -229 . DOI: 10.1007/s41019-025-00322-w
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Funding
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  • Natural Science Foundation of China(62172372; U23B2057; 62176185)
  • Postgraduate Education and Teaching Reform Special Project (Outstanding Engineers) of Nanjing University of Aeronautics and Astronautics(2024YJXGG-Z12)
Appendix
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Year 2026 volume 11 Issue 1
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37
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Article Info
doi: 10.1007/s41019-025-00322-w
  • Receive Date：2025-03-18
  • Online Date：2026-08-06
  • Published：2026-03-01
Article Data
Affiliations
History
  • Received：2025-03-18
  • Revised：2025-08-30
  • Accepted：2025-09-25
Funding
Natural Science Foundation of China(62172372; U23B2057; 62176185)
Postgraduate Education and Teaching Reform Special Project (Outstanding Engineers) of Nanjing University of Aeronautics and Astronautics(2024YJXGG-Z12)
Affiliations
    1Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Key Laboratory of Intelligent Decision and Digital Operations, Ministry of Industrial and Information Technology, College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China
    2Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
    3The School of Information Technology & Electric Engineering, The University of Queensland, Brisbane, Australia
    4College of Computer Science and Technology, Shandong Normal University, Jinan, China
    5Polytechnic Institute, Zhejiang University, Hangzhou, China
    6College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Corresponding:

✉ Bohan Li　
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