Humming-query and reinforcement-learning based modeling approach for personalized music recommendation

Music recommendation is a prominent application of recommender systems, which has been attracting more and more attentions. There are two research streams of music recommender systems: one is static recommendation based on learning user's preference according to historical data, and the other is dynamic recommendation considering user's feedback. But the individual music preference for a certain moment is closely related to personal experience of the music and music literacy, as well as temporal scenario with diversity. Thus, it's necessary to design a new music recommendation framework by integrating static recommendation and dynamic recommendation. Therefore, we propose a novel approach for music recommendation HRRS (Humming-Query and Reinforcement-Learning based Recommender Systems) by integrating prior two research streams. This novel recommendation framework HRRS based on humming query and reinforcement learning is learning and adapting to user's current preference continually by collecting interactive data in real time. This preliminary recommendation framework captures song characters, personal dynamic preferences, and yields a better listening experience with proper interaction.