Design of Deep Learning Network Model for Personalized Music Emotional Recommendation

Abstract

Music is a way for people to express their inner thoughts, and it is an art form to express their feelings and send their emotions. In modern society, people tend to listen to music more and more as a way of leisure and entertainment, and different types of music hold different feelings of listeners and trigger different emotional resonances. In this study, we propose an algorithmic model based on the two-layer attention mechanism, which includes the processing of textual convolutional neural network for music name and music label text data and the processing of two-layer attention mechanism, where the two-layer attention mechanism refers to the first layer of attention mechanism that learns the user's preference for each music feature from the feature level and the second layer of attention mechanism that learns the user's preference for each piece of music in the history listening list from the item level. The experiments show that the NDCG value of this method is improved by about 0.08, and the overall quality of the recommendation list is improved, which indicates that the user interest model constructed based on the fusion of various dings has good characterization ability and is helpful to alleviate data sparsity.