Research On Movie Recommendation System Based On Content And Collaborative Filtering Algorith

With the rapid development of the Internet,the increasing amount of information makes it difficult for users to find high-quality and efficient information that meets their needs.Taking the film industry as an example,there are currently many types of movie resources on the network,and users may spend a lot of time to find movies that match their preferences.The personalized movie recommendation system aims to accurately recommend movies that users may be interested in by analyzing each user’s behavior data.In this paper,the Movielens-1m dataset is selected to study the hybrid recommendation based on Content-based recommendation algorithm and collaborative filtering algorithm,and then the traditional collaborative filtering recommendation algorithm is compared with the traditional collaborative filtering recommendation algorithm.The experimental results show that the mixed recommendation effect based on Content-based recommendation algorithm and collaborative filtering algorithm is better than that of traditional collaborative filtering algorithm,and has relatively better evaluation index performance,which can provide users with better movie recommendation services.In this study,a personalized movie recommendation system based on B/S architecture is constructed.The system background uses Django development technology,the front-end mainly uses the Vue.js framework to build,and My SQL is selected as the system website database,which can realize many functions such as user login registration,movie search,user comments,and user recommendations.