Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning

Electronics

Chanhee Lee¹  Heuiseok Lim¹  Seolhwa Lee²  Dongyub Lee²  Jaechoon Jo³ 

[1] Seoul 02841, Korea;Department of Computer Science and Engineering, Korea University;Department of Smart Information and Communication Engineering, Sangmyung University, Seoul 31066, Korea;

关键词: deep learning;    convolutional neural network (cnn);    generative adversarial network (gan);    image2vec;    fashion recommendation;   

DOI  :  10.3390/electronics9030508

来源: DOAJ

【 摘 要 】

The digitization of the fashion industry diversified consumer segments, and consumers now have broader choices with shorter production cycles; digital technology in the fashion industry is attracting the attention of consumers. Therefore, a system that efficiently supports the searching and recommendation of a product is becoming increasingly important. However, the text-based search method has limitations because of the nature of the fashion industry, in which design is a very important factor. Therefore, we developed an intelligent fashion technique based on deep learning for efficient fashion product searches and recommendations consisting of a Sketch-Product fashion retrieval model and vector-based user preference fashion recommendation model. It was found that the “Precision at 5” of the image-based similar product retrieval model was 0.774 and that of the sketch-based similar product retrieval model was 0.445. The vector-based preference fashion recommendation model also showed positive performance. This system is expected to enhance consumers’ satisfaction by supporting users in more effectively searching for fashion products or by recommending fashion products before they begin a search.

【 授权许可】

Unknown