融合图像与文本构建属性图，提升时尚推荐精度并缓解冷启动问题
Personalized Fashion Recommendation with Image Attributes and Aesthetics Assessment
- 用图像和文本联合构建细粒度属性图，增强特征表达
- 在IQON3000数据集上达到与基线相当的推荐准确率
- 适合关注个性化推荐与新商品冷启动场景的研究者
个性化时尚推荐面临两大挑战：一是用户审美偏好影响决策，以往方法常忽略此因素；二是新商品不断上线，导致基于身份（ID）的推荐方法出现严重冷启动问题。这些新品对追求潮流的消费者尤为重要。本文通过将图像等信息转化为两个聚焦于优化利用与降噪建模的属性图，旨在提升推荐准确性并解决冷启动问题。相比以往将图像与文本分离处理的方法，本工作整合二者信息构建更丰富的属性图。借助大语言模型与视觉模型的能力，采用两种不同提示词高效提取所需细粒度属性。在IQON3000数据集上的初步实验表明，所提方法在推荐准确率上优于或接近现有基线。
原文摘要 · Abstract (English)
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.
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