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arXiv · 2410.11327

Sequential LLM Framework for Fashion Recommendation

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·Lian Xiong
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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.

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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, Lian Xiong. 2024-10-15. Sequential LLM Framework for Fashion Recommendation. https://arxiv.org/abs/2410.11327

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