Sequential LLM Framework for Fashion Recommendation

TL;DR

A sequential fashion recommendation framework that leverages a pre-trained large language model (LLM) enhanced with recommendation-specific prompts and introduces a novel mix-up-based retrieval technique for translating text into relevant product suggestions is proposed.

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.

Figures (6)

• Figure 1: Examples highlighting fashion characteristics. Figure (a) illustrates the extensive color variations in fashion products, while Figure (b) demonstrates the seasonality attributes of fashion items.
• Figure 2: An overview of our method.
• Figure 3: The demonstration example of our prompt.
• Figure 4: Performance comparison between our method with baselines in cold-start settings.
• Figure 5: Performance comparison between our method with baselines in zero-shot settings.