Sequential LLM Framework for Fashion Recommendation
This work addresses fashion recommendation challenges for online retailers, but it appears incremental as it builds on existing LLM and recommendation methods.
The authors tackled the problem of fashion recommendation by proposing a sequential framework that uses a pre-trained large language model with recommendation-specific prompts and a novel mix-up-based retrieval technique, resulting in significant performance enhancements as shown in extensive experiments.
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.