1. Sequential LLM Framework for Fashion Recommendation
- Author
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Liu, Han, Tang, Xianfeng, Chen, Tianlang, Liu, Jiapeng, Indu, Indu, Zou, Henry Peng, Dai, Peng, Galan, Roberto Fernandez, Porter, Michael D, Jia, Dongmei, Zhang, Ning, and Xiong, Lian
- Subjects
Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Machine Learning - 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.
- Published
- 2024