Analysis of the Performance Forms and Techniques of Dynamic Performing Arts and Musical Performances in Musicals Based on Improved Neural Collaborative Filtering

The performance of a musical is an artistic expression that takes into account both singing and drama, and it is a musical journey for the characters to achieve their goals under the prescribed situation given by the text of the musical. In-depth analysis of the situational performance of musical theatre singing, and discussing how musical theatre actors support the dramatic performance of musical theatre singing through situation establishment, character relationship processing and drama reinforcement are hot topics in the field of music research. Based on the improved neural collaborative filtering technology, this paper attempts to extract song content features from the macro attributes of music (genre, artist, etc.), and uses the Word2vec method to train from the original high-dimensional sparse feature vectors to obtain dense low-dimensional hidden feature vectors. The collaborative filtering, matrix decomposition and content recommendation methods based on the proposed method are integrated into the framework of deep learning technology, and an improved neural collaborative filtering music recommendation model is proposed to provide personalized song recommendations for each user. In terms of model structure, on the one hand, all features are grouped, and comprehensive group features are obtained after processing to enhance the expressive ability of the model; on the other hand, interactive features are added to the model through the outer product of vectors to learn more nonlinear information. Experiments are carried out on two music datasets. The experimental results show that the F1 and mAP of the model in this paper are much higher than other models, so the recommendation quality is significantly better than other models. In addition, due to the introduction of user information and song attributes, the cold start problem is alleviated, and the use of Word2vec method also effectively alleviates the problem of data sparsity, and the use of deep learning methods greatly improves the generalization ability and robustness of the model.