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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
Publication Date: 2020/02/01
Online ISSN: 1745-1361
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.