Personalized fashion recommendation is a difficult task because 1) the decisions are highly correlated with users' aesthetic appetite, which previous work frequently overlooks, and 2) many new items are constantly rolling out that cause strict cold-start problems in the popular identity (ID)-based recommendation methods. These new items are critical to recommend because of trend-driven consumerism. In this work, we aim to provide more accurate personalized fashion recommendations and solve the cold-start problem by converting available information, especially images, into two attribute graphs focusing on optimized image utilization and noise-reducing user modeling. Compared with previous methods that separate image and text as two components, the proposed method combines image and text information to create a richer attributes graph. Capitalizing on the advancement of large language and vision models, we experiment with extracting fine-grained attributes efficiently and as desired using two different prompts. Preliminary experiments on the IQON3000 dataset have shown that the proposed method achieves competitive accuracy compared with baselines.

本研究解决了个性化时尚推荐中的两个主要问题：用户的审美偏好往往被忽视，以及新商品带来的冷启动问题。通过将图像信息转化为优化的属性图，结合图像和文本信息，我们提出了一种新方法，初步实验表明，该方法在推荐准确性上与现有方法具有竞争力。

基于图像属性和美学评估的个性化时尚推荐