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International Conference on Web Information Systems Engineering

WISE 2018: Web Information Systems Engineering – WISE 2018 pp 51–66Cite as

Geographical Proximity Boosted Recommendation Algorithms for Real Estate

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11234)

Abstract

China’s real estate sector has become the major force for the rapid growth of China’s economy. There is a great demand for the real estate applications to provide users with their personalized property recommendations to alleviate information overloading. Unlike the recommendation problems in traditional domains, the real estate recommendation has its unique characteristics: users’ preferences are significantly affected by the locations (e.g. school district housing) and prices of those properties. In this paper, we propose two geographical proximity boosted real estate recommendation models. We capture the relations between the latent feature vectors of real estate items by utilizing the average-based and individual-based geographical regularization terms. Both terms are integrated with the weighted regularized matrix factorization framework to model users’ implicit feedback behaviors. Experimental results on a real-world data set show that our proposed real estate recommendation algorithms outperform the traditional methods. Sensitivity analysis is also carried out to demonstrate the effectiveness of our models.

Keywords

  • Real estate
  • Geographical proximity
  • Weighted regularized matrix factorization
  • Recommender systems

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Notes

  1. 1.

    http://china.soufun.com/.

  2. 2.

    http://www.fang.com/.

  3. 3.

    http://www.house365.com/.

  4. 4.

    http://lbsyun.baidu.com.

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Acknowledgments

The authors would like to acknowledge the support for this work from the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (Grant No. 17KJB520028), NUPTSF (Grant No. NY217114) and Qing Lan Project of Jiangsu Province.

Author information

Authors and Affiliations

  1. College of Tongda, Nanjing University of Posts and Telecommunications, Nanjing, China

    Yonghong Yu

  2. School of Information and Communication Technology, Griffith University, Gold Coast, Australia

    Can Wang

  3. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Li Zhang

  4. School of Computer Science, Hubei University of Technology, Wuhan, China

    Rong Gao

  5. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia

    Hua Wang

Authors
  1. Yonghong Yu
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  2. Can Wang
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  3. Li Zhang
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  4. Rong Gao
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  5. Hua Wang
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Corresponding author

Correspondence to Yonghong Yu .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. University of Victoria, Footscray, VIC, Australia

    Hua Wang

  4. University of New South Wales, Sydney, NSW, Australia

    Hye-Young Paik

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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Yu, Y., Wang, C., Zhang, L., Gao, R., Wang, H. (2018). Geographical Proximity Boosted Recommendation Algorithms for Real Estate. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_4

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  • DOI: https://doi.org/10.1007/978-3-030-02925-8_4

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