@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://preview.aclanthology.org/add-emnlp-2024-awards/2024.emnlp-industry.95/",
    doi = "10.18653/v1/2024.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."
}