Modeling real estate for school district identification

Fei Tan, Chaoran Cheng, Zhi Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The affiliated school district of a real estate property is often a crucial concern. How to automate the identification of residential homes located in a favorable educational environment, however, is largely unexplored until now. The availability of heterogeneous estate-related data offers a great opportunity for this task. Nevertheless, it is such heterogeneity that poses significant challenges to their amalgamation in a unified fashion. To this end, we develop G-LRMM model to integrate digital price, textual comments, and geographical location information together. The proposed approach is able to capture the in-depth interaction among multi-Type data greatly. The evaluation on the dataset of Beijing property market justifies the benefits of our approach over baselines. The further comparison among different components is also conducted and demonstrates their important roles. Moreover, the proposed model can offer useful insights into modeling heterogeneous data sources.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1227-1232
Number of pages6
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jan 31 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
CountrySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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