@inproceedings{kim-etal-2026-ucgrec,
title = "{UCGR}ec: User-Centric Graph Learning for {LLM}-based Sequential Recommendation",
author = "Kim, HanBeul and
Na, CheolWon and
Bae, Suyoung and
Choi, YunSeok and
Lee, Jee-Hyong",
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.175/",
pages = "3576--3591",
ISBN = "979-8-89176-395-1",
abstract = "Recently, Large Language Models (LLM) have emerged as a promising paradigm for sequential recommendation. In sequential recommendation, effectively integrating diverse user preferences is essential for improving LLM performance, as users often exhibit multiple interests across different contexts. However, most existing LLM-based methods rely primarily on item descriptions or utilize user preferences independently. As a result, they overlook the relationships among preferences and fail to filter out less-relevant items that introduce noise. This makes it difficult to accurately capture the user{'}s interests, leading to suboptimal recommendations. To overcome these limitations, we propose UCGRec (User-Centric Graph Learning for LLM-based Sequential Recommendation), a novel method that effectively integrates diverse user-relevant preference signals into a unified user-centric graph. Then, we inject the graph-based knowledge into the LLM through end-to-end training with graph neural networks. We conduct extensive experiments on four widely used sequential real-world recommendation datasets. Our experimental results demonstrate that UCGRec significantly outperforms conventional and state-of-the-art LLM-based methods."
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<abstract>Recently, Large Language Models (LLM) have emerged as a promising paradigm for sequential recommendation. In sequential recommendation, effectively integrating diverse user preferences is essential for improving LLM performance, as users often exhibit multiple interests across different contexts. However, most existing LLM-based methods rely primarily on item descriptions or utilize user preferences independently. As a result, they overlook the relationships among preferences and fail to filter out less-relevant items that introduce noise. This makes it difficult to accurately capture the user’s interests, leading to suboptimal recommendations. To overcome these limitations, we propose UCGRec (User-Centric Graph Learning for LLM-based Sequential Recommendation), a novel method that effectively integrates diverse user-relevant preference signals into a unified user-centric graph. Then, we inject the graph-based knowledge into the LLM through end-to-end training with graph neural networks. We conduct extensive experiments on four widely used sequential real-world recommendation datasets. Our experimental results demonstrate that UCGRec significantly outperforms conventional and state-of-the-art LLM-based methods.</abstract>
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%0 Conference Proceedings
%T UCGRec: User-Centric Graph Learning for LLM-based Sequential Recommendation
%A Kim, HanBeul
%A Na, CheolWon
%A Bae, Suyoung
%A Choi, YunSeok
%A Lee, Jee-Hyong
%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 kim-etal-2026-ucgrec
%X Recently, Large Language Models (LLM) have emerged as a promising paradigm for sequential recommendation. In sequential recommendation, effectively integrating diverse user preferences is essential for improving LLM performance, as users often exhibit multiple interests across different contexts. However, most existing LLM-based methods rely primarily on item descriptions or utilize user preferences independently. As a result, they overlook the relationships among preferences and fail to filter out less-relevant items that introduce noise. This makes it difficult to accurately capture the user’s interests, leading to suboptimal recommendations. To overcome these limitations, we propose UCGRec (User-Centric Graph Learning for LLM-based Sequential Recommendation), a novel method that effectively integrates diverse user-relevant preference signals into a unified user-centric graph. Then, we inject the graph-based knowledge into the LLM through end-to-end training with graph neural networks. We conduct extensive experiments on four widely used sequential real-world recommendation datasets. Our experimental results demonstrate that UCGRec significantly outperforms conventional and state-of-the-art LLM-based methods.
%U https://aclanthology.org/2026.findings-acl.175/
%P 3576-3591
Markdown (Informal)
[UCGRec: User-Centric Graph Learning for LLM-based Sequential Recommendation](https://aclanthology.org/2026.findings-acl.175/) (Kim et al., Findings 2026)
ACL