Movie Recommendation System Based on User-Based Collaborative Filtering

With the increasing popularity of networking, we are often bombarded with information, making it difficult to choose. Therefore, how to quickly and accurately find what we like from the massive information is a problem worth attention. This paper designs a movie recommendation system, first explaining the purpose and significance of this design, then introducing relevant recommendation algorithms, focusing on collaborative filtering algorithms, researching the technical requirements for system implementation, and finally implementing the entire recommendation system. The system uses the 'ml-latest-small' dataset in movielens and crawls movie details. Python is used as the main development language, and the system uses user-based collaborative filtering algorithm to implement movie recommendations, including the following main functions: user registration and login, personal information maintenance, classification display, movie search, movie collection, movie rating, movie evaluation, movie recommendation, etc., providing users with excellent movie recommendation services.