Room Style Estimation for Style-Aware Recommendation

Abstract

Interior design is a complex task as evident by multitude of professionals, websites, and books, offering design advice. Additionally, such advice is highly subjective in nature since different experts might have different interior design opinions. Our goal is to offer data-driven recommendations for an interior design task that reflects an individual's room style preferences. We present a style-based image suggestion framework to search for room ideas and relevant products for a given query image. We train a deep neural network classifier by focusing on high volume classes with high-agreement samples using a VGG architecture. The resulting model shows promising results and paves the way to style-aware product recommendation in virtual reality platforms for 3D room design.