| Leveraging proficiency and preference for online Karaoke recommendation |
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| 作者姓名： | Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA |
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| 作者单位： | Anhui Province Key Laboratory of Big Data Analysis and Application;School of Computer and Information;Eller College of Management;iFlyTek Research |
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| 基金项目： | grants from the National Key Research and Development Program of China(2016YFB1000904);the National Natural Science Foundation of China(Grant Nos.61325010 and U 1605251);the Fundamental Research Funds for the Central Universities of China(WK2350000001);Le Wu gratefully acknowledges the support of the Open Project Program of the National Laboratory of Pattern Recognition(201700017);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). |
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| 摘 要： | 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 singers5 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 recommendation model 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 we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating matrix.Finally,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.
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| 关 键 词： | KTV matrix FACTORIZATION RECOMMENDATION system |
Leveraging proficiency and preference for online Karaoke recommendation |
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| Ming HE,Hao GUO,Guangyi LV,Le WU,Yong GE,Enhong CHEN,Haiping MA.Leveraging proficiency and preference for online Karaoke recommendation[J].Frontiers of Computer Science,2020,14(2):273-290. |
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| Authors: | Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA |
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| Affiliation: | 1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China2. School of Computer and Information, Hefei University of Technology, Hefei 230026, China3. Eller College of Management, The University of Arizona, Arizona 85721-0108, USA4. 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. |
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| Keywords: | KTV matrix factorization recommendation system |
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