Design and Implementation of Movie Recommendation System Based on Naive Bayes

Abstract

Today, with the rapid development of the Internet industry, the era of big data has arrived. What follows is that the surge in data volume has brought great challenges to people's rapid and timely screening of useful information. Based on this highly realistic problem, the recommendation system that solves the user's individual needs is born. In this paper, based on the theory of Naive Bayes, the MovieLens data set is used for testing. The user's scoring data are used for similar analysis to generate the user's similarity matrix, and the target user can be personalized. Compared with other algorithms, this paper has a good recommendation effect.