UCGRec: User-Centric Graph Learning for LLM-based Sequential Recommendation

ACL ARR 2026 January Submission8867 Authors

Keywords: Large Language Models, Graph, Recommendation, LLM Recommendation

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: NLP Recommendation, LLM Recommendation

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 8867