2020.06.09 孟昕報告

摘要：
Recent years have witnessed a growing trend of building the capsule wardrobe by minimizing and diversifying the garments in their messy wardrobes. Thanks to the recent advances in multimedia techniques, many researches have promoted the automatic creation of capsule wardrobes by the garment modeling. Nevertheless, most capsule wardrobes generated by existing methods fail to consider the user profile, including the user preferences, body shapes and consumption habits, which indeed largely affects the wardrobe creation. To this end, we introduce a combinatorial optimizationbased personalized capsule wardrobe creation framework, named PCW-DC, which jointly integrates both garment modeling (i.e., wardrobe compatibility) and user modeling (i.e., preferences, body shapes). To justify our model, we construct a dataset, named bodyFashion, which consists of 116, 532 user-item purchase records on Amazon involving 11,784 users and 75,695 fashion items. Extensive experiments on bodyFashion have demonstrated the effectiveness of our proposed model. As a byproduct, we have released the codes and the data to facilitate the research community.