Fashion Advisor: Product Recommendation System

Abstract

Abstract: In the fashion domain, predicting compatibility is a significantly difficult task due to its subjective nature. Previous work in this domain focuses on comparing product images rather than a real-world scene. This fails to capture key context like body type, seasons, and other occasions in the scene. This is an important use case which needs to be addressed. Here, we propose a task ’Fashion Advisor’ which deals with measuring compatibility between a real-world scene and a product. We use two compatibility scores, global and local where global compatibility considers the overall scene and the local compatibility focuses on finer details of the scene using category guided attention mechanism. There were many different baseline methods compared with the proposed method and the proposed method gives promising results on Fashion and Home Datasets.