Multi-modal clothing recommendation model based on large model and VAE enhancement
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 3 Oct 2024.
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13 of 43 references.
References · 13
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