Accurately recommending products has long been a subject requiring in-depth research. This study proposes a multimodal paradigm for clothing recommendations. Specifically, it designs a multimodal analysis method that integrates clothing description texts and images, utilizing a pre-trained large language model to deeply explore the hidden meanings of users and products. Additionally, a variational encoder is employed to learn the relationship between user information and products to address the cold start problem in recommendation systems. This study also validates the significant performance advantages of this method over various recommendation system methods through extensive ablation experiments, providing crucial practical guidance for the comprehensive optimization of recommendation systems.

翻译：精准推荐产品长期以来都是一个需要深入研究的课题。本研究提出了一种用于服装推荐的多模态范式。具体而言，设计了一种融合服装描述文本与图像的多模态分析方法，利用预训练大语言模型深入挖掘用户与产品的隐含语义。此外，采用变分编码器学习用户信息与产品之间的关系，以应对推荐系统中的冷启动问题。本研究还通过大量消融实验验证了该方法相较于多种推荐系统方法所具有的显著性能优势，为推荐系统的全面优化提供了重要的实践指导。