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Frontiers of Computer Science

, Volume 14, Issue 2, pp 273–290 | Cite as

Leveraging proficiency and preference for online Karaoke recommendation

  • Ming He
  • Hao Guo
  • Guangyi Lv
  • Le Wu
  • Yong Ge
  • Enhong ChenEmail author
  • Haiping Ma
Research Article

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 

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Notes

Acknowledgments

The authors thank Qi Liu for valuable suggestions, and thank Liying Zhang for her help to polish English writing of this paper. This research was partially supported by grants from the National Key Research and Development Program of China (2016YFB1000904), the National Natural Science Foundation of China (Grant Nos. 61325010 and U1605251), and the Fundamental Research Funds for the Central Universities of China (WK2350000001). LeWu gratefully acknowledges the support of the Open Project Program of the National Laboratory of Pattern Recognition (201700017), and the Fundamental Research Funds for the Central Universities (JZ2016HGBZ0749). Yong Ge acknowledges the support of the National Natural Science Foundation of China (NSFC, Grant Nos. 61602234 and 61572032).

Supplementary material

11704_2018_7072_MOESM1_ESM.pdf (327 kb)
Leveraging proficiency and preference for online Karaoke recommendation

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Ming He
    • 1
  • Hao Guo
    • 1
  • Guangyi Lv
    • 1
  • Le Wu
    • 2
  • Yong Ge
    • 3
  • Enhong Chen
    • 1
    Email author
  • Haiping Ma
    • 4
  1. 1.Anhui Province Key Laboratory of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiChina
  3. 3.Eller College of ManagementThe University of ArizonaArizonaUSA
  4. 4.iFlyTek ResearchHefeiChina

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