Machine Learning Based Personalized Movie Research and Implementation of Recommendation System

With the development of the Internet industry, the information age presents a trend of “information overload”, and people's efficiency in extracting effective information is getting lower and lower. In order to relieve people's browsing pressure, this paper implements a collaborative filtering algorithm based on machine learning for the movie recommendation, citing the principle of personalized recommendation system proposed by Robert Armstrong and others in the United States in 1995. First, the rating data is preprocessed and visualized in consideration of the user's real behavior. Then implement the algorithm mentioned above, and use the test indicators to measure the performance of the recommender system and optimize the system parameters. Finally, using software engineering and Java front-end knowledge based on Spring+SpringMVC+Mybaits (SSM) to conduct demand analysis, functional analysis, non-functional analysis and establish a database. At last, use java database connectivity (JDBC) to link the database Mysql, and finally realized a movie recommender system with basic functions.