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  • Geographical Proximity Boosted Recommendation Algorithms for Real Estate

    Author(s)
    Yu, Y
    Wang, C
    Zhang, L
    Gao, R
    Wang, H
    Griffith University Author(s)
    Wang, Can
    Year published
    2018
    Metadata
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    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 ...
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    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.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    11234 LNCS
    DOI
    https://doi.org/10.1007/978-3-030-02925-8_4
    Subject
    Pattern recognition
    Data mining and knowledge discovery
    Publication URI
    http://hdl.handle.net/10072/381407
    Collection
    • Conference outputs

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