Geographical Proximity Boosted Recommendation Algorithms for Real Estate

Yonghong Yu, Can Wang, Li Zhang, Rong Gao, Hua Wang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2018
PublisherSpringer
Pages51-66
ISBN (Electronic)978-3-030-02925-8
ISBN (Print)978-3-030-02924-1
DOIs
Publication statusPublished - 2018

Publication series

NameWeb Information Systems Engineering – WISE 2018
Volume11234
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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