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

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.

Smart Processing for Systems under Uncertainty or Perturbation

“…Research on algorithm implementation was carried out in [1][2][3]. Yang et al propose two deep neural networks based on the attention mechanism to improve a sequence-topoint learning model.…”

Fashion Recommendation Systems, Models and Methods: A Review

“…Guan et al studied these features using image recognition, product attribute extraction and feature encoding. Researchers have also considered user features such as facial features, body shapes, personal choice or preference, locations and wearing occasions in predicting users' fashion interests [31,[75][76][77][78]. A well-defined user profile can differentiate a more personalized or customized recommendation system from a conventional system [28,79].…”

“…The models most used in developing fashion recommendation systems are multilayer perceptron (MLP), recurrent neural network (RNN), k-nearest neighbor (kNN), convolutional neural networks (CNN), Bayesian networks, generative adversarial network (GAN) and autoencoder (AE) [8,12,31,51,86,103,[153][154][155][156][157][158]. Researchers modified the algorithms and tuned the hyperparameters to different extents to increase the prediction accuracy.…”

“…Fashion recommendation systems (FRSs) generally provide specific recommendations to the consumer based on their browsing and previous purchase history. Social-network-based FRSs consider the user's social circle, fashion product attributes, image parsing, fashion trends, and consistency in fashion styles as important factors since they impact upon the user's purchasing decisions [28][29][30][31][32][33][34][35][36][37][38]. FRSs have the ability to reduce transaction costs for consumers and increase revenue for retailers.…”

“…Social-networkbased recommendation [7,8,31,43,92,133,[138][139][140][141][142][143][144][145][146][147][148][149][150][151][152] • Personalized clothing recommendation built using three types of data:…”