Music depth recommendation algorithm based on user data mining

Mass information will generate too many choices while providing services, making it difficult for users to make choices. How to analyze user needs based on users' historical listening records to achieve song recommendation is one of the challenging topics in the field of music recommendation. Existing music recommendation methods simply use all the music that the user has listened to as the context of music recommendation, resulting in the same context weights learned for different types of music, which seriously affects the accuracy of music recommendation. In order to improve the accuracy and personalization of music classification, this paper proposes the use of a deep music recommendation algorithm based on user data mining technology in music classification. Tendency, construct behavior feature space, and combine factor decomposition machine theory to predict user behavior types, as the basis for personalized music recommendation.