Design And Implementation Of Music Recommendation System Based On Factorization Machine

With the development and popularization of multimedia technology and Internet technology,online digital entertainment has quickly integrated into people’s lives,and technology has profoundly changed the production methods of digital entertainment products and people’s consumption and entertainment models.At the same time,the production methods of digital entertainment products and the formation of new consumption models have greatly improved the production efficiency of digital entertainment products,and various audio-visual entertainment products have shown an explosive growth trend.Faced with a huge ocean of information,how to use new technologies to intelligently select suitable products for people,including entertainment products,has become a real demand.In this era,the recommendation system has become a popular tool for people to provide information matching services.In response to this problem,the paper implements a music recommendation system that pursues high availability and high stability.First of all,the paper has an in-depth understanding and discussion of the advantages and disadvantages of the widely used recommendation algorithm.By analyzing the data sparsity problem,cold start problem,scalability problem and computational complexity problem in the collaborative filtering algorithm,the paper realize A music recommendation system based on factorization machine and other recommendation algorithms is introduced.The algorithm can learn in a sparse environment,and effectively reduces the computational complexity,and solves the problem of data sparsity and scalability.For the cold start problem,the system incorporates content-based recommendation and heat-based recommendation algorithm to enrich the system’s recommendation perspective.At the same time,the paper applies the design concept of microservices,splits the system functions,and gives the design of each module,using the Springboot framework,some components of the Spring Cloud Alibaba solution,the Vue JS framework,and FM algorithm,including a variety of recommendations Algorithms and other technologies have realized a music recommendation system.After systematic testing,the system operates normally and can accurately recommendpersonalized recommendations for customers.Each part of the system is independent of each other,and can complete the design function through RPC calls,which meets the user’s needs for selecting music.