Sequential LLM Framework for Fashion Recommendation
Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Peng Zou, Peng Dai, Roberto Fernandez Galan
Abstract
The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience. While recommendation systems have been widely studied, most are designed for general e-commerce problems and struggle with the unique challenges of the fashion domain. To address these issues, we propose a sequential fashion recommendation framework that leverages a pre-trained large language model (LLM) enhanced with recommendation-specific prompts. Our framework employs parameter-efficient fine-tuning with extensive fashion data and introduces a novel mix-up-based retrieval technique for translating text into relevant product suggestions. Extensive experiments show our proposed framework significantly enhances fashion recommendation performance.
BibTeX
@inproceedings{liu-etal-2024-sequential,
title = "Sequential {LLM} Framework for Fashion Recommendation",
author = "Liu, Han and
Tang, Xianfeng and
Chen, Tianlang and
Liu, Jiapeng and
Indu, Indu and
Zou, Henry Peng and
Dai, Peng and
Galan, Roberto Fernandez and
Porter, Michael D and
Jia, Dongmei and
Zhang, Ning and
Xiong, Lian",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.95/",
doi = "10.18653/v1/2024.emnlp-industry.95",
pages = "1276--1285"
}