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Recommender Systems in the Real Estate Market—A Survey

Applied sciences, 2021-08, Vol.11 (16), p.7502 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app11167502

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  • Title:
    Recommender Systems in the Real Estate Market—A Survey
  • Author: Gharahighehi, Alireza ; Pliakos, Konstantinos ; Vens, Celine
  • Subjects: Collaboration ; Decision making ; Electronic commerce ; Information sources ; Literature reviews ; Real estate ; Recommender systems ; User generated content
  • Is Part Of: Applied sciences, 2021-08, Vol.11 (16), p.7502
  • Description: The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app11167502
  • Source: Open Access: DOAJ Directory of Open Access Journals
    AUTh Library subscriptions: ProQuest Central
    ROAD: Directory of Open Access Scholarly Resources
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