Design of a Music Recommendation Model on the Basis of Multilayer Attention Representation

Abstract

Current music recommendation systems can explore the general relationship between the users and songs to recommend music to the users; however, they cannot distinguish the different preferences of different users for the same song. For example, a user may like a song because of the singer, while another user will like it not for the singer but just because of the composition of the song or its melody. A recommender system that knows this difference would be more effective in recommending music to the users. To this end, this paper proposes a music recommendation model based on multilayer attention representation, which learns song representations from multidimensions using user-attribute information and song content information, and mines the preference relationship between users and songs. In order to distinguish the differences in user preferences for multidomain features of songs, a feature-dependent attention network is designed; in order to distinguish the differences in user preferences for different historical behaviors and to explore the temporal dependence of user behaviors, a song-dependent attention network is designed. Finally, the SoftMax function is used to calculate the distribution of users' preferences for candidate songs and is used to generate recommendations. The experimental results on 30Music and MIGU datasets show that the proposed model achieves significant improvement in recall and MRR compared with the current recommendation models.