Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit

Abstract

The methods used to predict how highly a person will evaluate a product or a group of people are known as recommendation systems. Books, movies, restaurants, and other products can be among the things commonly recommended. Objects where people differ in their preferences matter a lot. For predicting the preferences 2 methods are used, one a content based approach that considers the characteristic of an item, and the other is a collaborative method that evaluates choices by taking into account previous user behaviour. In this paper, a system for recommending fashion items is proposed, one that will base its recommendations on the provided clothing images' styles. The upper body and lower body clothing images, as well as those of a human model, are the main focus of this work. We have used the fashion image dataset from Kaggle website. This paper presents an idea to develop a content based recommendation system that uses convolutional neural network model, ResNet-50.