A Personalized Recommendation System for Housing Information Based on Deep Learning

In view of the problems of complex housing information in the real estate market and the difficulty for users in finding houses, the personalized recommendation system of housing information based on deep learning is studied. The system adopts a hierarchical architecture. The data layer collects multi-source data from the housing information platform and stores it in MySQL and MongoDB databases respectively according to the structural characteristics. The preprocessing layer improves the data quality by removing noise, processing stopped words and eliminating duplicate data. The recommendation layer uses the convolutional neural network to generate the recommendation results. The service layer realizes invalid housing filtering, grouping and sorting optimization, and recommendation reason adding functions. The experiment shows that the recommendation reliability of the system is high, and significantly improves the efficiency of user house hunting, greatly shortens the house search and transaction time, and has a good effect of house information recommendation.