Classical music recommendation algorithm on art market audience expansion under deep learning

Abstract

Abstract The purpose of the study is to help users know about their favorite music and expand art market audiences. First, the personalized recommendation data of classical music are obtained based on the deep learning recommendation algorithm technology, artificial intelligence, and music playback software of users. Second, a systematic experiment is conducted on the improved recommendation algorithm, and a classical music dataset is established and used for model training and user testing. Then, the network model of the classical music recommendation algorithm is constructed through the typical convolutional neural network model, and the optimal parameters suitable for the model are found. The experimental results show that the optimal value of the dimension in the hidden layer is 192, and 24,000 training rounds can converge to the global optimum when the learning rate is 0.001. The personalized recommendation is provided for target users by calculating the similarity between user preference and potential features of classical music, relieving the auditory fatigue of art market audiences, improving user experience, and expanding the art market audience through the classical music recommendation system.