A Collaborative Filtering Based Personalized TOP-K Recommender System for Housing

Electronic information resource has become the main way for users obtaining information. Facing the huge amount of information in the real estate market, traditional methods are difficult to meet the users’ effective information needs. How to dig out from the mass of information to the appropriate information is a difficult and time-consuming problem for anyone. How can personalized recommender system solve the problem? In this paper, the authors proposed an algorithm named Collaborative Filtering Based Personalized TOP-K Recommender system for Housing (CFP-TR4H), and a personalized recommender system based on CFP-TR4H is also designed in this manuscript. A case study on Nanjing (a city in China) real estate market is also conducted to discuss and validate the effectiveness of our method.