Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2026
Abstract
The cold-start problem remains a significant challenge in recommendation systems, particularly for new users or unseen items with little to no historical data. Existing methods, including graph neural networks, often struggle in such scenarios. Inspired by the success of transformer models in natural language processing, we propose G-TRAC (Graph-Textual Representations Alignment for Cold-start Recommendations), a novel approach that integrates transformer-based textual modeling with graph neural networks. By effectively leveraging both textual and structural information, G-TRAC addresses cold-start challenges more effectively. Extensive experiments demonstrate its ability to enhance recommendation quality and generalize well across diverse scenarios.
Keywords
Cold-Start Recommendation, Graph Neural Networks, Language Models, Recommender System
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
WSDM '26: Proceedings of the 19th ACM International Conference on Web Search and Data Mining, February 22-26, Boise, ID
First Page
1089
Last Page
1093
ISBN
9798400722929
Identifier
10.1145/3773966.3779358
Publisher
Association for Computing Machinery
City or Country
New York
Citation
CHANG, Li Yang; FANG, Yuan; TSAI, Ming Feng; and WANG, Chuan Ju.
G-TRAC: Graph-textual representations alignment for cold-start recommendations. (2026). WSDM '26: Proceedings of the 19th ACM International Conference on Web Search and Data Mining, February 22-26, Boise, ID. 1089-1093.
Available at: https://ink.library.smu.edu.sg/sis_research/11075
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/10.1145/3773966.3779358