Recommendation System in E-house Mobile Application

In recent years, real estate domain provided huge amount of data and
information of property on the Internet. User is getting trouble because they
spend lot of time and efforts to search for good properties match their need,
then contact with several peoples and set up meeting to give final decision.
The solution of this has led to the development of recommender system,
which is a powerful technique that apply knowledge discovery and
understand user behavior based on user data/rating on past-experience to
solve the current problem, generate recommended property that suit user
taste. This Final Year Project 2 report aim to develop a Recommender System
that can explore and automated suggest property to user. Hence, literature
review discussed about the importance and efficiency of Recommendation
System in modern world and methodology demonstrate an implementation of
Recommender based on property data sets that author collected from multiple
users by learning their preference, process it and suggest list of properties that
most suitable. The system is developed by using Surprise Python package
framework. Throughout the results of this project, author compared between
two most common method for the establishing of Recommender that are top�N and kNN. Each method has advantages and disadvantages but overall top�N give better performance and fit within real estate property system model.
Recommender System represent a good basis need to property market and
bring solutions to many problems for better application and enhance quality
of life.