@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",
pages = "1276--1285",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Sequential LLM Framework for Fashion Recommendation
%A Liu, Han
%A Tang, Xianfeng
%A Chen, Tianlang
%A Liu, Jiapeng
%A Indu, Indu
%A Zou, Henry Peng
%A Dai, Peng
%A Galan, Roberto Fernandez
%A Porter, Michael D.
%A Jia, Dongmei
%A Zhang, Ning
%A Xiong, Lian
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F liu-etal-2024-sequential
%X 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.
%U https://aclanthology.org/2024.emnlp-industry.95
%P 1276-1285
Markdown (Informal)
[Sequential LLM Framework for Fashion Recommendation](https://aclanthology.org/2024.emnlp-industry.95) (Liu et al., EMNLP 2024)
ACL
- Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Peng Zou, Peng Dai, Roberto Fernandez Galan, Michael D Porter, Dongmei Jia, Ning Zhang, and Lian Xiong. 2024. Sequential LLM Framework for Fashion Recommendation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1276–1285, Miami, Florida, US. Association for Computational Linguistics.