TY - GEN
T1 - Modeling real estate for school district identification
AU - Tan, Fei
AU - Cheng, Chaoran
AU - Wei, Zhi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85014563057&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014563057&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2016.43
DO - 10.1109/ICDM.2016.43
M3 - Conference contribution
AN - SCOPUS:85014563057
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1227
EP - 1232
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
Y2 - 12 December 2016 through 15 December 2016
ER -