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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2020, Vol. 14 Issue (2) : 273-290    https://doi.org/10.1007/s11704-018-7072-6
RESEARCH ARTICLE
Leveraging proficiency and preference for online Karaoke recommendation
Ming HE1, Hao GUO1, Guangyi LV1, Le WU2, Yong GE3, Enhong CHEN1(), Haiping MA4
1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China
2. School of Computer and Information, Hefei University of Technology, Hefei 230026, China
3. Eller College of Management, The University of Arizona, Arizona 85721-0108, USA
4. iFlyTek Research, Hefei 230026, China
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Abstract

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.

Keywords KTV      matrix factorization      recommendation system     
Corresponding Author(s): Enhong CHEN   
Just Accepted Date: 24 November 2017   Online First Date: 17 September 2019    Issue Date: 16 October 2019
 Cite this article:   
Ming HE,Hao GUO,Guangyi LV, et al. Leveraging proficiency and preference for online Karaoke recommendation[J]. Front. Comput. Sci., 2020, 14(2): 273-290.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-7072-6
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I2/273
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