Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2026
Abstract
In this work, we propose VerbaLightGCN, a novel LLM-based recommendation framework that integrates the semantic understanding of LLMs with user-item interaction modeling. Traditional collaborative filtering (CF) models typically embed user and item IDs into a latent space to capture interaction signals. However, pretrained LLMs cannot natively interpret these learned embeddings. To bridge this gap, VerbaLightGCN adopts a CF-as-text paradigm, in which collaborative signals are encoded in textual form and directly learned from the user–item interaction graph, and are then combined with semantic information to construct user and item profiles that function as latent embeddings. Inspired by LightGCN, our method retains its message-passing design but replaces numerical embedding computations with a Chain-of-Thought prompting mechanism. This enables LLMs to simulate the LightGCN aggregation process through natural language. The result is a recommendation framework that unifies semantic understanding with collaborative signals in a fully language-native form. Experiments show that VerbaLightGCN achieves superior performance to both zero-shot LLM-based and traditional CF-based baselines. Further analysis reveals that the user and item profiles generated by VerbaLightGCN effectively capture both semantic preferences and collaborative filtering signals.
Keywords
Chain-of-thought, Collaborative filtering, Interaction graph, Large language models (LLMs), LightGCN, Recommendation system, User modeling
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
SIGIR '26: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, July 20-24
First Page
1393
Last Page
1403
ISBN
9798400725999
Identifier
10.1145/3805712.3809621
Publisher
ACM
City or Country
New York
Citation
NGO HUU, Manh-Khanh and LAUW, Hady Wirawan.
Verbalizing LightGCN: Direct learning of textual representations from user-item interaction graph via LLMs. (2026). SIGIR '26: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, July 20-24. 1393-1403.
Available at: https://ink.library.smu.edu.sg/sis_research/11320
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3805712.3809621