Development of fashion product retrieval and recommendations model based on deep learning

Jaechoon Jo, Seolhwa Lee, Chanhee Lee, Dongyub Lee, Heuiseok Lim

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number508
JournalElectronics (Switzerland)
Volume9
Issue number3
DOIs
Publication statusPublished - 2020 Mar

Keywords

  • Convolutional neural network (cnn)
  • Deep learning
  • Fashion recommendation
  • Generative adversarial network (gan)
  • Image2vec

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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