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

• KATO Naoki

  The University of Tokyo

• YAMASAKI Toshihiko

  The University of Tokyo

• AIZAWA Kiyoharu

  The University of Tokyo

• OHAMA Takemi

  Ietty Co., Ltd.

Abstract

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.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E103.D (2), 398-405, 2020-02-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• floor plan
• machine learning
• prediction
• preference
• real estate

Details

• CRID: 1390565134825160576
• NII Article ID: 130007793586
• DOI: 10.1587/transinf.2019edp7146
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E103.D/2/E103.D_2019EDP7146/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed