Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations
This work addresses style replication discrepancies in fashion recommendations for users, but it appears incremental as it builds on existing LLM techniques with domain-specific adaptations.
The paper tackles the problem of fashion recommendation systems faltering under distribution shifts by introducing the Fashion Large Language Model (FLLM), which integrates domain knowledge and uses auto-prompt generation and retrieval augmentation to enhance personalized advice, resulting in improved accuracy, interpretability, and few-shot learning capabilities compared to existing models.
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.