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Leveraging proficiency and preference for online Karaoke recommendation

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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.

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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).

Author information

Authors and Affiliations

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, 230027, China

    Ming He, Hao Guo, Guangyi Lv & Enhong Chen

  2. School of Computer and Information, Hefei University of Technology, Hefei, 230026, China

    Le Wu

  3. Eller College of Management, The University of Arizona, Arizona, 85721-0108, USA

    Yong Ge

  4. iFlyTek Research, Hefei, 230026, China

    Haiping Ma

Authors
  1. Ming He
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  2. Hao Guo
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  4. Le Wu
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  5. Yong Ge
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  6. Enhong Chen
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  7. Haiping Ma
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Corresponding author

Correspondence to Enhong Chen.

Additional information

Ming He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China. His major research interests include recommendation system, user behavioral analysis, and machine learning. He has published several papers in refereed conference proceedings and journals, such as CIT’15, DASFAA’16, KSEM’17 and TWEB.

Hao Guo is currently a research engineer of Living Analytics Research Centre (LARC), School of Information System, Singapore Management University, Singapore. He received the MS degree of Computer Science from the University of Science and Technology of China, China in 2017 and the BE degree of Software Engineering from Northeastern University, China in 2014. His research interests lie in recommendation system and natural language processing, with an emphasis on deep recommendation system, question answering and reinforcement learning.

Guangyi Lv received the BE degree in Computer Science and Technology in 2013 from Sichuan University, China. He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China. His major research interests include deep learning, natural language processing, and recommendation system. He has published several papers in refereed conference proceedings, such as AAAI’16, AAAI’17 and PAKDD’15.

Le Wu is currently a Faculty Member with the Hefei University of Technology (HFUT), China. She received the PhD degree in Computer Science from University of Science and Technology of China (USTC), China. Her general area of research is data mining, recommender system, and social network analysis. She has published several papers in referred journals and conferences, such as TKDE, TIST, AAAI, IJCAI, KDD, SDM and ICDM. Dr. Le Wu is the recipient of the Best of SDM 2015 Award.

Yong Ge is an assistant professor of Management Information Systems in University of Arizona, USA. He received the PhD degree in information technology from Rutgers, The State University of New Jersey, USA in 2013. His research interests include data mining and business analytics. He received the ICDM-2011 Best Research Paper Award. He has published prolifically in refereed journals and conference proceedings, such as TKDE, TOIS, TKDD, ACM SIGKDD, SIAM SDM, IEEE ICDM and ACM RecSys.

Enhong Chen is a professor and vice dean of the School of Computer Science at USTC, China. He received the PhD degree from USTC, China. His general area of research includes data mining and machine learning, social network analysis and recommender systems. He has published more than 100 papers in refereed conferences and journals, including IEEE Trans. KDE, IEEE Trans. MC, KDD, ICDM, NIPS and CIKM. He was on program committees of numerous conferences including KDD, ICDM, SDM. His research is supported by the National Science Foundation for Distinguished Young Scholars of China. He is a senior member of the IEEE.

Haiping Ma is a head of Big Data Research in IFLYTEK CO., LTD.. She received the PhD degree in information technology from University of Science and Technology of China, China. Her research interests include user behavior modeling and computational advertising. She has published in refereed journals and conference proceedings, such as WWW, CIKM, ICDM, KSEM, Neurocomputing and IJITDM.

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He, M., Guo, H., Lv, G. et al. Leveraging proficiency and preference for online Karaoke recommendation. Front. Comput. Sci. 14, 273–290 (2020). https://doi.org/10.1007/s11704-018-7072-6

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