Llm-generated content-based explanations for user experience in fashion recommender systems

Abstract

Recommendation explanations are crucial in helping users make informed and confident decisions, especially in domains such as fashion, where personal style and preferences play an important role. While previous studies have predominantly used review data for explanations, the review-based method requires the availability and quality of a good number of reviews. To address this issue, we investigate the effectiveness of content-based recommendation explanations in fashion recommender systems. Using a Large Language Model (LLM) and deep learning techniques trained on fashion attribute data, we developed a framework that extracts essential visual information from product images and generates user-tailored explanations. This approach allows us to generate customized explanations at various levels—basic, simple, and detailed—for each recommendation. We developed a My Own Style (MOS) interface that displays fashion products, recommendations, and explanations. Our user study with 211 participants showed that detailed explanations, especially when combined with diversity-based algorithms, significantly improved user satisfaction and trust in fashion recommendations. This study contributes to clothing and textile research by providing guidelines for fashion-specific LLM prompts and demonstrating the effectiveness of LLM-generated explanations in fashion e-commerce. Our findings point the way to more personalized and transparent AI-driven fashion recommender systems that improve user experience and style exploration in fashion e-commerce.