Abstract: In this paper, we present a practical system to automatically suggest the most pairing clothing items, given the reference clothing (upper-body or low-body). However this has being a challenge, due to having varieties of clothing categories. Clothing is one of the most informative cues for human appearance. In our daily life, people need to wear properly and beautifully to show their confidence, politeness and social status in various occasion. But, it is not easy to decide to decide on what and how to wear at the same time to match with the selected clothes. To address this problem, we propose a quadruple network architecture, where one dual network adopts Siamese convolution neural network architecture. Training examples are pairs of upper-body and low-body clothing items that are either compatible or incompatible. Another dual convolution neural network is used to learn clothing style features of the input image. This framework allows learning a feature transformation from the images of clothing items to two latent spaces, which we called compatible space and style space. After training the two dual networks, we use a distance fusion method to fuse the features extracted from the compatible and style dual networks. To acquire an optimized model and verify our proposed method, we expand a large clothing dataset called "How to Wear Beautifully" (H2WB). Experiments on H2WB dataset demonstrated that our learning model are effective with feature distance fusion and clothing item pairing recommendation.