Dress Well via Fashion Cognitive Learning
This addresses the need for more precise fashion recommendations for online shoppers by incorporating personal physical information, representing an incremental improvement over existing fashion compatibility models.
The paper tackles the problem of personalized fashion recommendations by incorporating personal physical information, proposing a Fashion Cognitive Network (FCN) that learns relationships between outfit compositions and individual appearance features. The results show that their framework outperforms alternative methods on the newly collected O4U dataset, providing strong qualitative and quantitative evidence.
Fashion compatibility models enable online retailers to easily obtain a large number of outfit compositions with good quality. However, effective fashion recommendation demands precise service for each customer with a deeper cognition of fashion. In this paper, we conduct the first study on fashion cognitive learning, which is fashion recommendations conditioned on personal physical information. To this end, we propose a Fashion Cognitive Network (FCN) to learn the relationships among visual-semantic embedding of outfit composition and appearance features of individuals. FCN contains two submodules, namely outfit encoder and Multi-label Graph Neural Network (ML-GCN). The outfit encoder uses a convolutional layer to encode an outfit into an outfit embedding. The latter module learns label classifiers via stacked GCN. We conducted extensive experiments on the newly collected O4U dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.