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In the rapidly evolving real estate industry, providing highly accurate and personalized recommendations has become paramount. Precision in these recommendations is crucial to meet the unique needs of potential buyers and ensure that sellers effectively reach their target audiences. By offering tailored property suggestions, user experience is significantly improved, leading to greater satisfaction and engagement for both buyers and sellers. This paper presents an innovative recommendation system designed for real estate websites. It aims to enhance user satisfaction and engagement through targeted property suggestions. Our proposed system leverages Elasticsearch in combination with machine learning techniques to deliver precise recommendations. We detail the system architecture and its processes. Evaluations conducted on real-world datasets demonstrate better recommendation accuracy and user engagement compared to traditional methods. This combination leads to significant improvements in real estate performance and opens up opportunities for future research.

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Optimizing Real Estate Recommendations with Elasticsearch and Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Fatma Sbiaa,
  • Nadia Boufares,
  • Sonia Kotel,
  • Ahmed Maalel

摘要

In the rapidly evolving real estate industry, providing highly accurate and personalized recommendations has become paramount. Precision in these recommendations is crucial to meet the unique needs of potential buyers and ensure that sellers effectively reach their target audiences. By offering tailored property suggestions, user experience is significantly improved, leading to greater satisfaction and engagement for both buyers and sellers. This paper presents an innovative recommendation system designed for real estate websites. It aims to enhance user satisfaction and engagement through targeted property suggestions. Our proposed system leverages Elasticsearch in combination with machine learning techniques to deliver precise recommendations. We detail the system architecture and its processes. Evaluations conducted on real-world datasets demonstrate better recommendation accuracy and user engagement compared to traditional methods. This combination leads to significant improvements in real estate performance and opens up opportunities for future research.