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.

中文翻译：

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利用熟练程度和偏好来推荐在线卡拉OK

最近，已经发布了许多在线卡拉OK（KTV）平台，音乐爱好者可以在这些平台上唱歌。同时，系统会根据用户的歌唱行为自动评估其熟练程度。向用户推荐大概的歌曲可以初始化歌手的参与并提高用户对这些平台的忠诚度。但是，由于这些平台的独特特性，这并非易事。首先，由于用户可能无法获得系统在他们喜欢的歌曲上获得的高分，因此如何平衡用户的喜好与用户对歌曲推荐唱歌的熟练程度之间的关系仍然悬而未决。其次，用户歌曲交互行为的稀疏性可能会极大地影响推荐任务。为了解决以上两个挑战，本文中，通过考虑歌唱数据的独特特征，我们提出了一种融合信息的歌曲推荐模型。具体来说，我们首先通过结合用户的歌唱行为和系统评估来设计一个伪评分矩阵，从而充分利用用户的喜好和熟练程度。然后通过在伪评价矩阵的矩阵分解过程中融合用户和歌曲的丰富信息来缓解数据稀疏性问题。最后，在真实数据集上的大量实验结果证明了我们提出的模型的有效性。然后，通过在伪评价矩阵的矩阵分解过程中融合用户和歌曲的丰富信息，缓解数据稀疏性问题。最后，在真实数据集上的大量实验结果证明了我们提出的模型的有效性。然后，通过在伪评价矩阵的矩阵分解过程中融合用户和歌曲的丰富信息，缓解数据稀疏性问题。最后，在真实数据集上的大量实验结果证明了我们提出的模型的有效性。