用大模型+领域知识，让时尚推荐更懂人、更准、更会解释。
Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations
- 用自动生成提示训练大模型，融合时尚领域知识。
- 在少样本场景下准确率超越现有方法，且推理过程可解释。
- 适合需要个性化推荐和风格理解的电商与穿搭应用。
时尚深受社会文化影响，人们常模仿网红和标志性人物的风格。传统时尚搭配方法多采用监督学习模仿风格偶像决策，但在分布变化时易出错，输入微小变化即引发风格偏差。大语言模型（LLM）因交互友好、对话能力强、推理能力突出，日益广泛应用。为此，我们提出时尚大语言模型（FLLM），通过自动提示生成训练策略增强个性化穿搭建议能力，并在推理阶段引入检索增强技术，使模型能更好适配个体偏好。实验表明，该方法在准确性、可解释性和少样本学习能力上均优于现有模型。
原文摘要 · Abstract (English)
Fashion, deeply rooted in sociocultural dynamics, evolves as individuals emulate styles popularized by influencers and iconic figures. In the quest to replicate such refined tastes using artificial intelligence, traditional fashion ensemble methods have primarily used supervised learning to imitate the decisions of style icons, which falter when faced with distribution shifts, leading to style replication discrepancies triggered by slight variations in input. Meanwhile, large language models (LLMs) have become prominent across various sectors, recognized for their user-friendly interfaces, strong conversational skills, and advanced reasoning capabilities. To address these challenges, we introduce the Fashion Large Language Model (FLLM), which employs auto-prompt generation training strategies to enhance its capacity for delivering personalized fashion advice while retaining essential domain knowledge. Additionally, by integrating a retrieval augmentation technique during inference, the model can better adjust to individual preferences. Our results show that this approach surpasses existing models in accuracy, interpretability, and few-shot learning capabilities.
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