MMusic: a hierarchical multi-information fusion method for deep music recommendation

With the explosive growth of music volume, music recommendation systems have become an important tool for online music platforms to alleviate the information overload problem. Through the use of deep learning, the multi-information fusion-based deep recommendation method has gained popularity in the field of music recommendation systems research. However, most existing studies only consider the different kinds of information of users or music and fail to capture information's internal and external associations. In this work, we propose a hierarchical multi-information fusion method for deep music recommendation (MMusic), to fully exploit the features of each type of information and to better learn the representation of users and music. Specifically, combined with the features of music recommendation, we identify various kinds of information describing users and music, respectively. Then, we learn about the interactions within and between different kinds of information for fusion. We conduct extensive experiments on the publicly available dataset NOWPLAYINGRS. The results show that MMusic achieves the best performance compared with the baselines, which verifies the effectiveness and rationality of our model.