@inproceedings{chen-etal-2026-duet,
title = "{DUET}: Joint Exploration of User{--}Item Profiles in Recommendation System",
author = "Chen, Yue and
Sun, Yifei and
Wang, Lu and
Yang, Fangkai and
Zhao, Pu and
Hong, Minjie and
Dong, Yifei and
He, Minghua and
Hu, Nan and
Zhang, Jianjin and
Dai, Zhiwei and
Zhan, Yuefeng and
Han, Weihao and
Sun, Hao and
Lin, Qingwei and
Deng, Weiwei and
Sun, Feng and
Zhang, Qi and
Rajmohan, Saravan and
Zhang, Dongmei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1076/",
pages = "21392--21406",
ISBN = "979-8-89176-395-1",
abstract = "Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommenders instead leverage natural-language representations that are easier to interpret and integrate with downstream reasoning modules. This paper studies how to construct effective textual profiles for users and items, and how to align them for recommendation.A central difficulty is that the best profile format is not known a priori: manually designed templates can be brittle and misaligned with task objectives. Moreover, generating user and item profiles independently may produce descriptions that are individually plausible yet semantically inconsistent for a specific user{--}item pair. We propose Duet, an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. Duet follows a three-stage procedure: it first turns raw histories and metadata into compact cues, then expands these cues into paired profile prompts and then generate profiles, and finally optimizes the generation policy with reinforcement learning using downstream recommendation performance as feedback. Experiments on three real-world datasets show that Duet consistently outperforms strong baselines, demonstrating the benefits of template-free profile exploration and joint user{--}item textual alignment. Project page: https://duet-rec.github.io/."
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<abstract>Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommenders instead leverage natural-language representations that are easier to interpret and integrate with downstream reasoning modules. This paper studies how to construct effective textual profiles for users and items, and how to align them for recommendation.A central difficulty is that the best profile format is not known a priori: manually designed templates can be brittle and misaligned with task objectives. Moreover, generating user and item profiles independently may produce descriptions that are individually plausible yet semantically inconsistent for a specific user–item pair. We propose Duet, an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. Duet follows a three-stage procedure: it first turns raw histories and metadata into compact cues, then expands these cues into paired profile prompts and then generate profiles, and finally optimizes the generation policy with reinforcement learning using downstream recommendation performance as feedback. Experiments on three real-world datasets show that Duet consistently outperforms strong baselines, demonstrating the benefits of template-free profile exploration and joint user–item textual alignment. Project page: https://duet-rec.github.io/.</abstract>
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%0 Conference Proceedings
%T DUET: Joint Exploration of User–Item Profiles in Recommendation System
%A Chen, Yue
%A Sun, Yifei
%A Wang, Lu
%A Yang, Fangkai
%A Zhao, Pu
%A Hong, Minjie
%A Dong, Yifei
%A He, Minghua
%A Hu, Nan
%A Zhang, Jianjin
%A Dai, Zhiwei
%A Zhan, Yuefeng
%A Han, Weihao
%A Sun, Hao
%A Lin, Qingwei
%A Deng, Weiwei
%A Sun, Feng
%A Zhang, Qi
%A Rajmohan, Saravan
%A Zhang, Dongmei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-duet
%X Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommenders instead leverage natural-language representations that are easier to interpret and integrate with downstream reasoning modules. This paper studies how to construct effective textual profiles for users and items, and how to align them for recommendation.A central difficulty is that the best profile format is not known a priori: manually designed templates can be brittle and misaligned with task objectives. Moreover, generating user and item profiles independently may produce descriptions that are individually plausible yet semantically inconsistent for a specific user–item pair. We propose Duet, an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. Duet follows a three-stage procedure: it first turns raw histories and metadata into compact cues, then expands these cues into paired profile prompts and then generate profiles, and finally optimizes the generation policy with reinforcement learning using downstream recommendation performance as feedback. Experiments on three real-world datasets show that Duet consistently outperforms strong baselines, demonstrating the benefits of template-free profile exploration and joint user–item textual alignment. Project page: https://duet-rec.github.io/.
%U https://aclanthology.org/2026.findings-acl.1076/
%P 21392-21406
Markdown (Informal)
[DUET: Joint Exploration of User–Item Profiles in Recommendation System](https://aclanthology.org/2026.findings-acl.1076/) (Chen et al., Findings 2026)
ACL
- Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, and Dongmei Zhang. 2026. DUET: Joint Exploration of User–Item Profiles in Recommendation System. In Findings of the Association for Computational Linguistics: ACL 2026, pages 21392–21406, San Diego, California, United States. Association for Computational Linguistics.