Smart Recommendation of Cloud Music Services Based on Contextualized Mentality Modeling and Beyond

Cloud music services have gained immense popularity as internet applications. However, the vast array of music products available on cloud platforms presents a challenge for users seeking to swiftly and accurately discover their desired music. Furthermore, the emotional and psychological preferences of listeners, as well as their mood in varying environmental contexts, significantly influence their artistic experience with music. Consequently, there is a pressing need to develop a music recommendation system within cloud music services that enhances search efficiency and accuracy. This paper proposes an intelligent recommendation approach that leverages preference modeling and context sensing to deliver precise recommendations. Initially, a neural experiment is conducted to analyze customer satisfaction with the cloud music service. Building upon this, a mental model is introduced, which captures users' psychological preferences and music preferences. Finally, we present a smart recommendation method that integrates emotional and rational intelligence for cloud music services. Experimental results demonstrate the satisfactory and effective assistance provided by our approach in helping customers discover music aligned with their preferences.