Users' Preference Prediction of Real Estate Properties Based on Floor Plan Analysis

Naoki KATO  Toshihiko YAMASAKI  Kiyoharu AIZAWA  Takemi OHAMA  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D    No.2    pp.398-405
Publication Date: 2020/02/01
Publicized: 2019/11/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7146
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
floor plan,  machine learning,  prediction,  preference,  real estate,  

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With the recent advances in e-commerce, it has become important to recommend not only mass-produced daily items, such as books, but also items that are not mass-produced. In this study, we present an algorithm for real estate recommendations. Automatic property recommendations are a highly difficult task because no identical properties exist in the world, occupied properties cannot be recommended, and users rent or buy properties only a few times in their lives. For the first step of property recommendation, we predict users' preferences for properties by combining content-based filtering and Multi-Layer Perceptron (MLP). In the MLP, we use not only attribute data of users and properties, but also deep features extracted from property floor plan images. As a result, we successfully predict users' preference with a Matthews Correlation Coefficient (MCC) of 0.166.