Sequential LLM Framework for Fashion Recommendation

TL;DR

This paper introduces a novel sequential fashion recommendation framework that leverages pre-trained large language models with specialized prompts and a mix-up retrieval technique, significantly improving recommendation accuracy in the fashion domain.

Contribution

It presents a new LLM-based framework with recommendation-specific prompts and a mix-up retrieval method tailored for fashion recommendation tasks.

Findings

Enhanced recommendation accuracy demonstrated through extensive experiments

Effective use of parameter-efficient fine-tuning with fashion data

Introduction of a novel mix-up-based retrieval technique

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.