Style-Based Outfit Recommendation

In this paper we propose a garment recommendation system that leverages emotive color information to give recommendations that adhere to a desired style. We leverage previous work by Shigenobu Kobayashi on how specific color combinations, that pertain to certain pre-defined styles, are able to convey specific emotions in human beings. Leveraging this information, we extend the classic general garment recommendation to a style-driven one, where the user can adapt the suggestions to a specific style that may be more appropriate for a specific social event. Here, first we train a generalized style classifier based on Kobayashi's color triplets, then we lever-age a recent memory network-based garment recommendation system to perform suggestions of bottom garments (e.g. skirts, trousers, etc.) given a user-defined top (e.g. a shirt, T-shirt, etc.). Suggestions are then processed to maintain only the ones that, according to our classifier, are coherent with the user defined style. Experiments show that our system is able to generalise on Kobayashi's color styles and that the recommendation system is able to propose garments that are in line with the user desire while also introducing diversity in the proposed garments.