An Intelligent Recommendation System for Improvisation Playing and Singing Built with Database Technology

In order to facilitate the retrieval of relevant information and provide users with an efficient method, the author uses database technology to propose an intelligent recommendation system for improvisation and singing. This algorithm supplements traditional search methods to a certain extent. Based on this study, a thorough analysis of the recommended methods was conducted through collaboration, and their specifications were carefully examined. Finally, a systematic software analysis of the model was conducted using B/S software, achieving personalized music recommendation. The results indicate that in order to provide song recommendations to the target user Xiao Ming, the system first calculates the similarity between Xiao Ming and other users in the database. Subsequently, the system selects the K users with the highest similarity score and retrieves their improvised playback time. Based on the investigation, the system hypothesized Xiaoming's preference for classical improvisation. Then calculate the similarity score for the last three users, namely Zhang San, Li Si, and Wang Wu. Finally, through frequent mining and evaluation of project set association rules, the system recommended the following songs to Xiao Ming: Nocturne, Scottish Dance, and Narrative Music. Conclusion: The system aims to provide a comprehensive set of services that effectively meet the recommendation needs of users. With this system, users can receive recommendations for various improvisation and singing styles, which can be customized according to each user's personal preferences.