Users' Preference Prediction of Real Estates Featuring Floor Plan Analysis using FloorNet
Naoki Kato, Toshihiko Yamasaki, Kiyoharu Aizawa, takemi ohama · 2018
In recent years, with the progress of e-commerce, recommendation for not only mass-produced daily items, such as books, but also special items that are not mass-produced has become an important task. In this study, we present an algorithm for real estate recommendation. There are no identical properties in the world, properties already occupied by someone else cannot be recommended, and users rent or buy properties only a few times in their lives. Therefore, automatic property recommendation is one of the most difficult tasks. In this study, we predict users' preference for properties, which is the first step of property recommendation, by combining content-based filtering and multilayer perceptron (MLP). In the MLP, we used not only attribute data of users and properties but also the deep features extracted from floor plan images of properties. As a result, we succeeded in predicting users' preference with an accuracy of 60.7%.