Optimize the outfit schedule utilizing the thermodynamical genetic algorithm and Bi-LSTM + VSE

Abstract

In recent years, the application of artificial intelligence technology to the fashion field has attracted attention. This research proposes a method of optimizing the fashion outfit schedule by acquiring the performance scores of outfits from a deep learning model that learns outfits composed of images of multiple clothes and accessories. In the proposed method, first, input the outfit that combines the clothes you have into the already learned Bi-LSTM + VSE model, and get the performance scores. Based on the scores, I created a list consisting of multiple outfits, that is, a mix and match clothing plan, using the Thermodynamical Genetic Algorithm (TDGA). We impose restrictions that the same outfit should not be used during the period, the same item should not be used within 3 days, and there should be no items that have never been used during the period. These restrictions make it is possible to create a mix and match clothing plan while considering diversity. To confirm the effectiveness of the proposed method as a recommendation system, numerical experiments were carried out taking real fashion item data as examples.