@inproceedings{liao-etal-2026-eliminating,
title = "Eliminating Out-of-Domain Recommendations in {LLM}-based Recommender Systems: A Unified View",
author = "Liao, Hao and
Zhang, Jiwei and
Lian, Jianxun and
Lu, Wensheng and
Wu, Mingqi and
Shuowangg and
Zhang, Yong and
Huang, Yitian and
Zhou, Mingyang and
Mao, Rui",
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.310/",
pages = "6251--6271",
ISBN = "979-8-89176-395-1",
abstract = "Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework that bridges the gap between retrieval and generation by instantiating three grounding paradigms under a single architecture: embedding-based retrieval, constrained generation over rewritten item titles, and discrete item-tokenizer generation. Using the same backbone LLM and prompts, we systematically compare these three views on public benchmarks. RecLM strictly eradicates OOD recommendations (OOD@10 = 0) across all variants, and the constrained generation variants RecLM-cgen and RecLM-token achieve overall state-of-the-art accuracy compared to both strong ID-based and LLM-based baselines. Our unified view provides a systematic basis for comparing three distinct paradigms to reduce item hallucinations, offering a practical framework to facilitate the application of LLMs to recommendation tasks. Source code is at https://github.com/microsoft/RecAI."
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<abstract>Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework that bridges the gap between retrieval and generation by instantiating three grounding paradigms under a single architecture: embedding-based retrieval, constrained generation over rewritten item titles, and discrete item-tokenizer generation. Using the same backbone LLM and prompts, we systematically compare these three views on public benchmarks. RecLM strictly eradicates OOD recommendations (OOD@10 = 0) across all variants, and the constrained generation variants RecLM-cgen and RecLM-token achieve overall state-of-the-art accuracy compared to both strong ID-based and LLM-based baselines. Our unified view provides a systematic basis for comparing three distinct paradigms to reduce item hallucinations, offering a practical framework to facilitate the application of LLMs to recommendation tasks. Source code is at https://github.com/microsoft/RecAI.</abstract>
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%0 Conference Proceedings
%T Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View
%A Liao, Hao
%A Zhang, Jiwei
%A Lian, Jianxun
%A Lu, Wensheng
%A Wu, Mingqi
%A Zhang, Yong
%A Huang, Yitian
%A Zhou, Mingyang
%A Mao, Rui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Shuowangg
%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 liao-etal-2026-eliminating
%X Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework that bridges the gap between retrieval and generation by instantiating three grounding paradigms under a single architecture: embedding-based retrieval, constrained generation over rewritten item titles, and discrete item-tokenizer generation. Using the same backbone LLM and prompts, we systematically compare these three views on public benchmarks. RecLM strictly eradicates OOD recommendations (OOD@10 = 0) across all variants, and the constrained generation variants RecLM-cgen and RecLM-token achieve overall state-of-the-art accuracy compared to both strong ID-based and LLM-based baselines. Our unified view provides a systematic basis for comparing three distinct paradigms to reduce item hallucinations, offering a practical framework to facilitate the application of LLMs to recommendation tasks. Source code is at https://github.com/microsoft/RecAI.
%U https://aclanthology.org/2026.findings-acl.310/
%P 6251-6271
Markdown (Informal)
[Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View](https://aclanthology.org/2026.findings-acl.310/) (Liao et al., Findings 2026)
ACL
- Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, and Rui Mao. 2026. Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6251–6271, San Diego, California, United States. Association for Computational Linguistics.