A Hybrid Music Recommendation System Based on Scene-State Perception Model

In recent years, the recommendation based on mobile users and the one based on context-aware have become popular topics in the field of the recommendation system. However, most of the music platforms need manual annotation of user scene which means if the user forgets to do that, the recommendation system may fail to work. In this paper, we propose a scene-sensing model based on Naive Bayesian classification which can be used to automatically locate the users' scene and predict their state in real time. Exactly established on the basis of user scene and life state, we propose a hybrid music recommendation system which combines the recommendation result of SVD++ collaborative filtering model and logical regression model which is used to predict the most recent popular music. Experimental results indicates that the hybrid recommendation system perform well on mobile users.