@inproceedings{dong-etal-2026-knowledge,
title = "Knowledge Bias in {LLM}-based Sequential Recommendation: Source, Persistence, and Mitigation",
author = "Dong, Xiaoyu and
Dai, Quanyu and
Chen, Nuo and
Liu, Qijiong and
Zixuan, Yue and
Xie, Xuyang and
Zhu, Jieming and
Dong, Zhenhua and
Wu, Xiao-Ming",
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.324/",
pages = "6858--6873",
ISBN = "979-8-89176-462-0",
abstract = "The integration of large language models (LLMs) into recommender systems has garnered attention due to their ability to leverage rich semantic representations. However, these models suffer from a limitation, knowledge bias, a systematic tendency to over-recommend items dominated by pretraining knowledge, regardless of actual user interests. Although recent studies have focused on fine-tuning LLMs to better align with the collaborative data, our investigation reveals that such fine-tuning fails to fundamentally correct this deep-seated bias. In this work, we propose ReKnow, a lightweight and efficient decoding-time alignment method to mitigate knowledge bias. Specifically, we quantify LLMs' knowledge towards biased items, then realign output probabilities to match the target data distribution. For validation, we provide both theoretical justifications and empirical results, including evaluation on two datasets, demonstrating that our {~}enhances the quality and diversity of LLM-based sequential recommendation. We have release our code[{\ensuremath{<}}https://anonymous.4open.science/r/ReKnow-B17E{\ensuremath{>}}] for others to reproduce the results."
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<abstract>The integration of large language models (LLMs) into recommender systems has garnered attention due to their ability to leverage rich semantic representations. However, these models suffer from a limitation, knowledge bias, a systematic tendency to over-recommend items dominated by pretraining knowledge, regardless of actual user interests. Although recent studies have focused on fine-tuning LLMs to better align with the collaborative data, our investigation reveals that such fine-tuning fails to fundamentally correct this deep-seated bias. In this work, we propose ReKnow, a lightweight and efficient decoding-time alignment method to mitigate knowledge bias. Specifically, we quantify LLMs’ knowledge towards biased items, then realign output probabilities to match the target data distribution. For validation, we provide both theoretical justifications and empirical results, including evaluation on two datasets, demonstrating that our enhances the quality and diversity of LLM-based sequential recommendation. We have release our code[\ensuremath<https://anonymous.4open.science/r/ReKnow-B17E\ensuremath>] for others to reproduce the results.</abstract>
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%0 Conference Proceedings
%T Knowledge Bias in LLM-based Sequential Recommendation: Source, Persistence, and Mitigation
%A Dong, Xiaoyu
%A Dai, Quanyu
%A Chen, Nuo
%A Liu, Qijiong
%A Zixuan, Yue
%A Xie, Xuyang
%A Zhu, Jieming
%A Dong, Zhenhua
%A Wu, Xiao-Ming
%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 dong-etal-2026-knowledge
%X The integration of large language models (LLMs) into recommender systems has garnered attention due to their ability to leverage rich semantic representations. However, these models suffer from a limitation, knowledge bias, a systematic tendency to over-recommend items dominated by pretraining knowledge, regardless of actual user interests. Although recent studies have focused on fine-tuning LLMs to better align with the collaborative data, our investigation reveals that such fine-tuning fails to fundamentally correct this deep-seated bias. In this work, we propose ReKnow, a lightweight and efficient decoding-time alignment method to mitigate knowledge bias. Specifically, we quantify LLMs’ knowledge towards biased items, then realign output probabilities to match the target data distribution. For validation, we provide both theoretical justifications and empirical results, including evaluation on two datasets, demonstrating that our enhances the quality and diversity of LLM-based sequential recommendation. We have release our code[\ensuremath<https://anonymous.4open.science/r/ReKnow-B17E\ensuremath>] for others to reproduce the results.
%U https://aclanthology.org/2026.findings-emnlp.324/
%P 6858-6873
Markdown (Informal)
[Knowledge Bias in LLM-based Sequential Recommendation: Source, Persistence, and Mitigation](https://aclanthology.org/2026.findings-emnlp.324/) (Dong et al., Findings 2026)
ACL
- Xiaoyu Dong, Quanyu Dai, Nuo Chen, Qijiong Liu, Yue Zixuan, Xuyang Xie, Jieming Zhu, Zhenhua Dong, and Xiao-Ming Wu. 2026. Knowledge Bias in LLM-based Sequential Recommendation: Source, Persistence, and Mitigation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 6858–6873, Budapest, Hungary. Association for Computational Linguistics.