Real Estate Recommendation Using Historical Data and Surrounding Environments

Full Text (PDF, 815KB), PP.33-39

Views: 0 Downloads: 0


Uchchash Barua 1 Md. Sabir Hossain 1 Mohammad Shamsul Arefin 1,*

1. Department of Computer Science & Engineering Chittagong University of Engineering & Technology, Raozan, Chittagong

* Corresponding author.


Received: 10 Nov. 2018 / Revised: 20 Mar. 2019 / Accepted: 21 Jun. 2019 / Published: 8 Sep. 2019

Index Terms

Real-estate, Point of interest, Top-k apartments, recommendation, collaborative filtering


Recommending appropriate things to the user by analyzing available data is becoming popular day by day. There are no sufficient researches on Real-estate recommendation with historical data and surrounding environments. We have collected real-estate, historical and point of interest (POI) data from the various sources. In this research, a hybrid filtering technique is used for recommending real-estate consisting of collaborative and content-based filtering. Generally, in every website user ratings are collected for the recommendation. But we have considered historical data and surrounding environments of a real-estate location for recommendation by which it will be easy for a user to decide that which place would be better for him/her. If any user request for any specific location then the system will find the POI data using google map API. Then the system will consider historical data of that area, got from the trusted sources. So considering the minimum price and optimal facilities, our system will recommend top-k real-estate. After extensive experiments on real and synthetic data, we have proved the efficiency of our proposed recommender system.

Cite This Paper

Uchchash Barua, Md. Sabir Hossain, Mohammad Shamsul Arefin, "Real Estate Recommendation Using Historical Data and Surrounding Environments", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.5, pp. 33-39, 2019. DOI:10.5815/ijieeb.2019.05.05


[1]Barua, Suborna, et al. "Housing Real Estate Sector in Bangladesh Present Status and Policies Implications." ASA University Review, vol. 4, no. 1, Jan. 2010,
[2]Atisha Sachan and Vineet Richariya,” Survey on Recommender System based on Collaborative Technique”, Department of Computer Science And Engineering, International journal of innovations in engineering and technology (IJIET), ISSN: 2319-1058, vol.2, issue 2, pp1-7, April 2013.
[3]Prem Melville and Vikas Sindhwani,” Recommender System”, IBM T.J. Watson Research Center, Yorktown Heights, pp 1-18.
[4]J. Gupta and J. Gadge, "Performance analysis of recommendation system based on collaborative filtering and demographics," 2015 International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, 2015, pp. 1-6.doi: 10.1109/ICCICT.2015.7045675
[5]F. Gao, Y. Li, L. Han and J. Ma, "InfoSlim: An Ontology-Content Based Personalized Mobile News Recommendation System," 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, 2009, pp. 1-4.doi: 10.1109/WICOM.2009.5300815
[6]P. Mathew, B. Kuriakose and V. Hegde, "Book Recommendation System through content based and collaborative filtering method," 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, 2016, pp. 47-52.doi: 10.1109/SAPIENCE.2016.7684166
[7]Anand Shanker Tewari, Abhay Kumar, Asim Gopal Barman,” Book Recommendation System Based On Combine Features Of Content Based Filtering And Association Rule Mining”,IEEE International Advance Computing Conference(IACC) ISSN:47992572, pp502 14th augest2014.
[8]Chhavi rana, sanjay kumar Jain,” Building a Book Recommender system using time based content Filtering”, University Institute of Engineering and Technology, ISSN: 2224-2872, Issue 2, Volume 11, 6 February2012.
[9]Z. Chang, Md. S. Arefin, Y. Morimoto, “Hotel Recommendation Based On Surrounding Environments” , 2nd IIAI International Conference on Advanced Applied Informatics, 2013
[10]Ding, Zhijun, et al. "Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems." ACM Computing Surveys, vol. 51, no. 1, 2018, pp. 1-28.
[11]V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang, “Collaborative location and activity recommendations with GPS history data”, In Proc. of the 19th international conference on Worldwide web, pp. 1029-1038, 2010.
[12]K. Kodama, Y. Iijima, X. Guo, and Y. Ishikawa, “Skyline queries based on user locations and preferences for making location-based recommendations”, In Proc. of LBSN, 2009, pp.9-16.
[13]Wang, Fan, et al. "Mining user preferences of new locations on location-based social networks: a multidimensional cloud model approach." Wireless Networks, vol. 24, no. 1, 2016, pp. 113-125.
[14]Chowdhury, Chondrima, et al. "Developing a framework for recommending TV shows." 2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT), 2017.
[15]EzEstate (2017, Jan 10). Historical data, Available at:
[16]Lee, Yunkyoung, "RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING" (2015). Master's Projects. 439.
[17]Gitlab, "Real Estate Recommendation System",, hosted on November 2017.