Leveraging proficiency and preference for online Karaoke recommendation

Recently, many online Karaoke (KTV) platforms have been released, where music lovers sing songs on these platforms. In the meantime, the system automatically evaluates user proficiency according to their singing behavior. Recommending approximate songs to users can initialize singers’ participation and improve users’ loyalty to these platforms. However, this is not an easy task due to the unique characteristics of these platforms. First, since users may be not achieving high scores evaluated by the system on their favorite songs, how to balance user preferences with user proficiency on singing for song recommendation is still open. Second, the sparsity of the user-song interaction behavior may greatly impact the recommendation task. To solve the above two challenges, in this paper, we propose an informationfused song recommendationmodel by considering the unique characteristics of the singing data. Specifically, we first devise a pseudo-rating matrix by combing users’ singing behavior and the system evaluations, thus users’ preferences and proficiency are leveraged. Then wemitigate the data sparsity problem by fusing users’ and songs’ rich information in the matrix factorization process of the pseudo-ratingmatrix. Finally, extensive experimental results on a real-world dataset show the effectiveness of our proposed model.