Abstract
PurposeThe purpose of this study is to solve the cold start problem caused by the lack of evaluation information about the products.Design/methodology/approachA recommendation system has been developed by using the image data of the clothing products, assuming that the user considers the visual characteristics importantly when purchasing fashion products. In order to evaluate the performance of the model developed in this study, it was compared with Random, Itempop, Matrix Factorization and Generalized Matrix Factorization models.FindingsThe newly developed model was able to cope with the cold start problem better than other models.Social implicationsA hybrid recommendation system has been developed that combines the existing recommendation system with deep learning to effectively recommend fashion products considering the user's taste.Originality/valueThis is the first research to improve the performance of fashion recommendation system using the deep learning model trained by the images of fashion products.
Published Version
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