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dc.contributor.authorYu, Y
dc.contributor.authorWang, C
dc.contributor.authorZhang, L
dc.contributor.authorGao, R
dc.contributor.authorWang, H
dc.contributor.editorHakim Hacid, Wojciech Cellary, Hua Wang, Hye-Young Paik, Rui Zhou
dc.description.abstractChina’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.
dc.publisherSpringer Nature Switzerland AG
dc.relation.ispartofconferencenameWISE 2018
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartoflocationDubai, United Arab Emirates
dc.relation.ispartofvolume11234 LNCS
dc.subject.fieldofresearchPattern recognition
dc.subject.fieldofresearchData mining and knowledge discovery
dc.titleGeographical Proximity Boosted Recommendation Algorithms for Real Estate
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can

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