Research And Implementation Of Clothing Recommender System Based On Emotional Semantics

With the rapid development of garment industry, clothing recommendation and retrieval system plays a more and more important role in choosing satisfied garments for consumers. Traditionally these systems only recommend by either clothing image text annotation or clothing image content, e.g. colors etc. Recently, some researchers start trying to recommend clothing from human sensibility based on Kansei Engineering. Kansei can express user’s emotional demand in high semantic level. However, Kansei Engineering methods suffer from lacking of dynamic support and result personalization due to the fact that Kansei data are collected from a large number of fixed predesigned questionnaires and the final conclusion is drawn by statistic calculation of those data. The main researched content of this paper is about the shirt recommendations in the background of shirt design.To tackle the lack of personalization, in this paper, we propose a personalized shirt recommendation system(PSRS abbr.) based on dynamic mapping between the low level features’ space of shirt images and the high level user’s emotional semantic space. Finally, the PSRS is implemented as a module of system that named shirt intelligence design system based on emotional semantics(SIDS abbr.).We build a common user emotion model based on Kobayashi’s color psychology theory, and automatically annotate all shirt images in database with this emotion model. Then we utilize the result of emotional annotation as the default recommendation of our system. To meet the personalized requirement of each individual and also take other affective factors of clothing choosing such as shape, texture etc. into consideration in the mean time, we collect and update user’s preference through interaction with the user, and then map from the low features space of the shirt image into the user’s high level emotional semantic space by Personalized Recommender engine. Thus we build our personalized user emotion model. Experiments show that by two or three rounds of interaction, our system’s recommendation can meet the personalized emotional requirement of the user well.To tackle the low efficiency of shirt design, we proposal the shirt intelligence DIY design based on emotional semantics, which is implemented in SIDS. In SIDS, user can freely splice and design shirts with the materials(such as shirt parts,color, pattern) which are recommended by the system according user’s high level emotional adjectives input. If the user don’t satisfy the current recommendations, he can use the interactive design tools(such as color design, pattern design, Liquify and Texturizer tools, etc.) to personalize his requirements as much as possible.