[2C3-OS-17] [Organized Session] OS-17

[2C3-OS-17-02] Preference Prediction using User/Property Features and Floorplan Images

〇Naoki Kato¹, Toshihiko Yamasaki¹, Kiyoharu Aizawa¹, Takemi Ohama² (1. The University of Tokyo, 2. ietty Co., Ltd.)

Keywords:Real Estate, Machine Learning, Preference, Prediction, Floorplan

Due to the advancement of E-commerce in recent years, recommendation for not only mass-produced daily items but also special items that are not mass-produced is an important task. In this research, we present an algorithm for real estate recommendation. There is no identical property in the world, properties already occupied by someone else can not be recommended, and users rent or buy properties only a few times in their lives. Therefore, automatic recommendation of property is one of the most difficult tasks. As the first step of property recommendation, we predict users' preference for properties by combining content-based filtering and multilayer perceptron (MLP). In the MLP, we used not only attribute data of users and properties but also deep features extracted from Floorplan images of properties. As a result, we succeeded in predicting preference with accuracy of 60.7%.