Research On Online Hip-Hop Dancing Course Recommendation System Based On MVC Architecture

The research significance of designing the recommendation system is to form an online hip-hop course recommendation system based on the MVC architecture,with the recommendation system as the carrier,and with hip-hop as the point of interest,and with the improved collaborative filtering algorithm.Utilizing the trend of internet development and the advantages of updating and iterating new web technologies,we aim to find solutions for the profitability of ance studios,the improvement of social members’ physical fitness,and the satisfaction of dance hobbies.The design of the system platform aims to quickly iterate products,meet the ever-changing needs of users,and improve user experience.The MVC framework pattern is used,and the idea of hierarchical design is adopted to make the entire course teaching resource platform more hierarchical,allowing for detailed division of the system and resources.In addition,the system incorporates the personalized recommendation algorithm-collaborative filtering algorithm.This thesis will improve the collaborative filtering algorithm,study the noise removal-quick adjustment model,provide users with more accurate recommendations,and improve the user experience.This essays will explore this issue through the following aspects:1.The overall architecture of the system is designed,functional modules are divided,and business processes are described.The idea of hierarchical system and MVC architecture are adopted to realize a dynamic program design,simplify the modification and expansion of the program after the sequence.2.Use a more targeted personalized recommendation algorithm-collaborative filtering algorithm.Automatically process content that is difficult for the computer to process from the user’s perspective,categorizing and scoring courses based on the user’s behavior,thereby achieving accurate recommendations.3.Improved collaborative filtering algorithm,using a three stage model of denoising and rapid adjustment to analyze user multi behavior and prepare to optimize the algorithm then.