@inproceedings{sun-etal-2026-lupe,
title = "{LUPE}: Long-History User Profile Evolution for {LLM}-based Recommendation",
author = "Sun, Yifei and
Wang, Lu and
Yang, Fangkai and
Zhao, Pu and
Li, Liqun and
Li, Mingzheng and
Zhang, Jianjin and
Sun, Jimeng and
Dai, Zhiwei and
Hu, Nan and
Zeng, Zhengxin and
Zhan, Yuefeng and
Han, Weihao and
Sun, Hao and
Lin, Qingwei and
Rajmohan, Saravan and
Deng, Weiwei and
Sun, Feng and
Zhang, Qi and
Zhang, Dongmei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.338/",
pages = "7110--7124",
ISBN = "979-8-89176-462-0",
abstract = "Textual user profiling is an effective paradigm for LLM-based recommendation, but existing methods usually treat profile generation as a one-shot summarization problem. This limitation becomes more severe in long-history recommendation, where early preference signals may be overcompressed and recent behaviors may be overemphasized. We propose LUPE, a Long-history User Profile Evolution framework for LLM-based recommendation. Instead of generating a static profile from the full history, LUPE maintains a persistent textual profile and updates it progressively over temporally ordered interaction chunks. This allows the model to preserve long-term preferences while incorporating new behavioral evidence. To learn how profiles should evolve, LUPE optimizes a profile evolution policy via reinforcement learning using feedback from a frozen downstream LLM-based recommender. This allows the model to directly align profile updates with recommendation utility rather than relying on heuristic or supervised profile targets. Experiments on Yelp, Amazon CDs and Vinyl, and Amazon Books show that LUPE consistently outperforms strong baselines on both rating prediction and ranking tasks. Further analyses show that explicit profile evolution is especially helpful for users with long and heterogeneous interaction histories. The code repository: https://github.com/LongHistoryProfile/long{\_}history{\_}profile."
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<abstract>Textual user profiling is an effective paradigm for LLM-based recommendation, but existing methods usually treat profile generation as a one-shot summarization problem. This limitation becomes more severe in long-history recommendation, where early preference signals may be overcompressed and recent behaviors may be overemphasized. We propose LUPE, a Long-history User Profile Evolution framework for LLM-based recommendation. Instead of generating a static profile from the full history, LUPE maintains a persistent textual profile and updates it progressively over temporally ordered interaction chunks. This allows the model to preserve long-term preferences while incorporating new behavioral evidence. To learn how profiles should evolve, LUPE optimizes a profile evolution policy via reinforcement learning using feedback from a frozen downstream LLM-based recommender. This allows the model to directly align profile updates with recommendation utility rather than relying on heuristic or supervised profile targets. Experiments on Yelp, Amazon CDs and Vinyl, and Amazon Books show that LUPE consistently outperforms strong baselines on both rating prediction and ranking tasks. Further analyses show that explicit profile evolution is especially helpful for users with long and heterogeneous interaction histories. The code repository: https://github.com/LongHistoryProfile/long_history_profile.</abstract>
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%0 Conference Proceedings
%T LUPE: Long-History User Profile Evolution for LLM-based Recommendation
%A Sun, Yifei
%A Wang, Lu
%A Yang, Fangkai
%A Zhao, Pu
%A Li, Liqun
%A Li, Mingzheng
%A Zhang, Jianjin
%A Sun, Jimeng
%A Dai, Zhiwei
%A Hu, Nan
%A Zeng, Zhengxin
%A Zhan, Yuefeng
%A Han, Weihao
%A Sun, Hao
%A Lin, Qingwei
%A Rajmohan, Saravan
%A Deng, Weiwei
%A Sun, Feng
%A Zhang, Qi
%A Zhang, Dongmei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-lupe
%X Textual user profiling is an effective paradigm for LLM-based recommendation, but existing methods usually treat profile generation as a one-shot summarization problem. This limitation becomes more severe in long-history recommendation, where early preference signals may be overcompressed and recent behaviors may be overemphasized. We propose LUPE, a Long-history User Profile Evolution framework for LLM-based recommendation. Instead of generating a static profile from the full history, LUPE maintains a persistent textual profile and updates it progressively over temporally ordered interaction chunks. This allows the model to preserve long-term preferences while incorporating new behavioral evidence. To learn how profiles should evolve, LUPE optimizes a profile evolution policy via reinforcement learning using feedback from a frozen downstream LLM-based recommender. This allows the model to directly align profile updates with recommendation utility rather than relying on heuristic or supervised profile targets. Experiments on Yelp, Amazon CDs and Vinyl, and Amazon Books show that LUPE consistently outperforms strong baselines on both rating prediction and ranking tasks. Further analyses show that explicit profile evolution is especially helpful for users with long and heterogeneous interaction histories. The code repository: https://github.com/LongHistoryProfile/long_history_profile.
%U https://aclanthology.org/2026.findings-emnlp.338/
%P 7110-7124
Markdown (Informal)
[LUPE: Long-History User Profile Evolution for LLM-based Recommendation](https://aclanthology.org/2026.findings-emnlp.338/) (Sun et al., Findings 2026)
ACL
- Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Liqun Li, Mingzheng Li, Jianjin Zhang, Jimeng Sun, Zhiwei Dai, Nan Hu, Zhengxin Zeng, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Feng Sun, Qi Zhang, and Dongmei Zhang. 2026. LUPE: Long-History User Profile Evolution for LLM-based Recommendation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7110–7124, Budapest, Hungary. Association for Computational Linguistics.