Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2026
Abstract
Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibility. Collaborative filtering (CF) provides a traditional solution for this, but it struggles with data-sparse scenarios and complex user-item-outfit relationships. Meanwhile, existing template-based approaches are constrained by rigid pre-designed structures. To bridge these research gaps, we introduce CFALR (Collaborative Filtering-Augmented Large Language Model for Recommendation), a novel framework that synergizes collaborative filtering with large language models for personalized outfit recommendation. Specifically, CFALR describes user-outfit interactions in natural language and leverages LLMs to capture fashion semantics while employing CF-enhanced embeddings to bridge the semantic space and the collaborative interaction spaces. Our technical contributions include: (1) the first LLM-based architecture specifically designed for personalized outfit recommendation, (2) a CF-augmented generative mechanism that efficiently navigates the extensive combination space of outfit items, and (3) trainable projection layers that optimally integrate relational and content features. Experiments on Polyvore and IQON benchmarks demonstrate CFALR's superior performance over both traditional CF-based and LLM-based methods in personalized fill-in-the-blank and personalized outfit generation tasks.
Keywords
Fashion Recommendation, Fashion Outfit Generation, Large Language Model, Personalized Recommendation
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
ACM Transactions on Information Systems
First Page
1
Last Page
29
ISSN
1046-8188
Identifier
10.1145/3829367
Publisher
Association for Computing Machinery (ACM)
Citation
DING, Yujuan; LIAO, Junrong; MA, Yunshan; BIN, Yi; FAN, Wenqi; CHUA, Tat-Seng; and LI, Qing.
CFALR: Collaborative filtering-augmented large language model for personalized fashion outfit recommendation. (2026). ACM Transactions on Information Systems. 1-29.
Available at: https://ink.library.smu.edu.sg/sis_research/11268
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/10.1145/3829367