User-friendly Interior Design Recommendation

Abstract

We propose a novel search engine that recommends a combination of furniture preferred by a user based on image features. In recent years, research on furniture search engines has attracted attention with the development of deep learning techniques. However, existing search engines mainly focus on the techniques of extracting similar furniture (items), and few studies have considered interior combinations. Even techniques that consider the combination do not take into account the preference of each user. They make recommendations based on the text data attached to the image and do not incorporate a judgmental mechanism based on differences in individual preference such as the shape and color of furniture. Thus, in this study, we propose a method that recommends items that match the selected item for each individual based on individual preference by analyzing images selected by the user and automatically creating a rule for a combination of furniture based on the proposed features.