融合图文与大模型，提升服装推荐准确率
Multi-modal clothing recommendation model based on large model and VAE enhancement
- 用大语言模型分析图文信息，挖掘用户与商品深层语义
- 引入变分自编码器缓解冷启动问题，提升推荐效果
- 实验证明该方法优于多种主流推荐算法，适合电商场景
精准推荐长期是研究重点。本文提出一种多模态服装推荐范式，通过融合服装描述文本与图像，利用预训练大语言模型深入挖掘用户与商品的隐含语义。同时，采用变分自编码器学习用户与商品间关系，以解决推荐系统中的冷启动问题。通过大量消融实验验证，该方法在性能上显著优于多种推荐系统方法，为推荐系统的综合优化提供了重要实践指导。
原文摘要 · Abstract (English)
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.
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