Intelligent Music Recommendation System: Using Algorithms to Improve the Accuracy of Personalized Music Experience

To ensure that users have a good time when using music services and that Intelligent Music Recommendation Systems (IMRS) work well, it is important to study effective methods of music recommendation and to build a reliable platform for online recommendations that work in real time. For the purpose of improving upon current music recommendation systems, which suffer from poor generalizability, inadequate classification efficiency, and inadequate signal feature capture. We propose the Deep Sleep Optimization (DSO) algorithm as a deep confidence network-based music recommendation system and utilize simulated testing to prove its efficacy. As a result, the IMRS-DSO was created to enhance the deep confidence network in order to create a music recommendation algorithm that makes use of massive amounts of data and intelligent optimization techniques. The method relies on perusing the system’s database for information about each song, including its genre, artist, and tracks. By analyzing these characteristics, the recommendation engine can find songs that are similar to the music the utilizes has entered and then provide suggestions for like songs. Music recommendation accuracy, memory, and coverage are entirely improved through the predicted strategy, which effectively meets the real-time requirements. Collaborative filtering (COL) is a system that can automatically uncover users’ future interests and is often used for highly personalized music suggestions. Improves the flaws of conventional music recommendation systems’ inadequate generalization, inadequate signal feature capture, and inadequate classification efficiency.