Design and Implementation of Music Recommendation System Based on Big Data Platform

With the development of network technology, music recommendation system has also developed rapidly, and online music platform has become the first choice for people to listen to music. However, the music recommendation system also faces some problems, such as data storage confusion, low computational efficiency, cold start and data sparsity caused by large data scale. The recommendation system is analyzed and studied, and a hybrid recommendation algorithm based on collaborative filtering is designed. Two channels of offline data transmission and real-time data transmission are designed to collect and transmit data; Secondly, the overall architecture of the music recommendation system is designed, and each functional module is designed and implemented. The music data warehouse is built, and the data is processed and stored hierarchically. Then preprocess the data to facilitate the calculation of the recommended model. Based on the improved algorithm and Hadoop distributed framework, the recommendation module is completed and the music recommendation system is implemented; Finally, the music recommendation system is tested to verify the feasibility and stability of the recommendation system, which reflects the efficiency, scalability and stability of the music recommendation system, and can meet the personalized music needs of users.