Visible to the public A Visual Similarity Recommendation System using Generative Adversarial Networks

TitleA Visual Similarity Recommendation System using Generative Adversarial Networks
Publication TypeConference Paper
Year of Publication2019
AuthorsAy, Betül, Aydın, Galip, Koyun, Zeynep, Demir, Mehmet
Conference Name2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)
Keywordscontent-based recommendation system, content-based retrieval, Convolutional codes, Deep Learning, deep learning network, e-commerce platform, feature extraction, Footwear, Generative Adversarial Learning, generative adversarial network based image retrieval system, generative adversarial networks, Generators, image retrieval, image similarity, learning (artificial intelligence), Metrics, neural nets, product image, pubcrawl, query item, recommendation system strategy, recommender systems, resilience, Resiliency, Scalability, Training, visual similarity recommendation system, visualization
Abstract

The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.

URLhttps://ieeexplore.ieee.org/document/8876932
DOI10.1109/Deep-ML.2019.00017
Citation Keyay_visual_2019