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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.

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Correspondence to Yonghong Yu .

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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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