Md. Sabir Hossain

Work place: Department of Computer Science & Engineering Chittagong University of Engineering & Technology, Raozan, Chittagong



Research Interests: Computational Science and Engineering, Software Engineering, Computational Learning Theory, Data Mining, Data Structures and Algorithms, Analysis of Algorithms, Algorithmic Complexity Theory, Mathematical Analysis, Computational Complexity Theory


Md. Sabir Hossain received his bachelor degree in computer science and engineering from Chittagong University of Engineering & Technology (2015) with an outstanding result. He has also completed his M.Sc. degree in computer science and engineering in 2019 from the same university. His research interests are algorithmic complexity analysis, data mining, machine learning, big data, and software engineering. He is currently working as a faculty member (Assistant Professor) in the same department of his university. He has a number of publications in international conferences as well as journals. Besides teaching and research activities, he also works as an independent freelancer.

Author Articles
Real Estate Recommendation Using Historical Data and Surrounding Environments

By Uchchash Barua Md. Sabir Hossain Mohammad Shamsul Arefin

DOI:, Pub. Date: 8 Sep. 2019

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.

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