Leveraging proficiency and preference for online Karaoke recommendation

doi:10.1007/s11704-018-7072-6

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

Download: PDF(1072 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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

1	L Backstrom, J Leskovec. Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the 4th ACMInternational Conference onWeb Search and DataMining. 2011, 635–644 https://doi.org/10.1145/1935826.1935914
2	B McFee, L Barrington, G Lanckriet. Learning content similarity for music recommendation. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(8): 2207–2218 https://doi.org/10.1109/TASL.2012.2199109
3	N H Liu. Comparison of content-based music recommendation using different distance estimation methods. Applied Intelligence, 2013, 38(2): 160–174 https://doi.org/10.1007/s10489-012-0363-y
4	D Bogdanov, M Haro, F Fuhrmann, E Gómez, P Herrera. Contentbased music recommendation based on user preference examples. Copyright Information, 2010, 33: 1–6
5	C Guan, Y J Fu, X J Lu, H Xiong, E H Chen, Y L Liu. Vocal competence based karaoke recommendation: a maximum-margin joint model. In: Proceedings of 2016 SIAM International Conference on Data Mining. 2016, 135–143 https://doi.org/10.1137/1.9781611974348.16
6	M Soleymani, A Aljanaki, F Wiering, R C Veltkamp. Content-based music recommendation using underlying music preference structure. In: Proceedings of 2015 IEEE International Conference on Multimedia and Expo. 2015, 1–6 https://doi.org/10.1109/ICME.2015.7177504
7	C Guo. Feature generation and selection on the heterogeneous graph for music recommendation. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016, 715 https://doi.org/10.1145/2835776.2855088
8	O Celma. The exploit-explore dilemma in music recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, 377 https://doi.org/10.1145/2959100.2959122
9	T H Song, Z H Peng, S Z Wang, W J Fu, X G Hong, P S Yu. Based cross-domain recommendation through joint tensor factorization. In: Proceedings of International Conference on Database Systems for Advanced Applications. 2017, 525–540 https://doi.org/10.1007/978-3-319-55753-3_33
10	X Wu, Q Liu, E H Chen, L He, J S Lv, C Cao, G P Hu. Personalized next-song recommendation in online karaokes. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 137–140 https://doi.org/10.1145/2507157.2507215
11	R Jin, Y J Chai, L Si. An automatic weighting scheme for collaborative filtering. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004, 337–344 https://doi.org/10.1145/1008992.1009051
12	J L Herlocker, J A Konstan, L G Terveen, J T Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 2004, 22(1): 5–53 https://doi.org/10.1145/963770.963772
13	B Sarwar, G Karypis, J Konstan, J Riedl. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. 2001, 285–295 https://doi.org/10.1145/371920.372071
14	Y Koren, R Bell, C Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30–37 https://doi.org/10.1109/MC.2009.263
15	V Sindhwani, S Bucak, J Hu, A Mojsilovic. A family of non-negative matrix factorizations for one-class collaborative filtering problems. In: Proceedings of the ACM Conference on Recommender Systems. 2009
16	J T Liu, C H Wu, W Y Liu. Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decision Support Systems, 2013, 55(3): 838–850 https://doi.org/10.1016/j.dss.2013.04.002
17	H Ma, H X Yang, R M Lyu, I King. Sorec:social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008, 931–940 https://doi.org/10.1145/1458082.1458205
18	H Ma, I King, R M Lyu. Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 203–210 https://doi.org/10.1145/1571941.1571978
19	M Jamali, M Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 135–142 https://doi.org/10.1145/1864708.1864736
20	X W Yang, H Steck, Y Liu. Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 1267–1275 https://doi.org/10.1145/2339530.2339728
21	H Li, D M Wu, W B Tang, N Mamoulis. Overlapping community regularization for rating prediction in social recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems. 2015, 27–34 https://doi.org/10.1145/2792838.2800171
22	D Eck, P Lamere, T Bertin-Mahieux, S Green. Automatic generation of social tags for music recommendation. In: Proceedings of the 20th International Conference on Neural Information Processing Systems. 2008, 385–392
23	Y Yan, T L Liu, Z Y Wang. A music recommendation algorithm based on hybrid collaborative filtering technique. In: Proceedings of Chinese National Conference on Social Media Processing. 2015, 233–240 https://doi.org/10.1007/978-981-10-0080-5_23
24	T Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. 2003, 259–266 https://doi.org/10.1145/860435.860483
25	L Si, R Jin. Flexible mixture model for collaborative filtering. In: Proceedings of the 20th International Conference on Machine Learning. 2003, 704–711
26	N N Liu, Q Yang. Eigenrank: a ranking-oriented approach to collaborative filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 83–90 https://doi.org/10.1145/1390334.1390351
27	Q Liu, E H Chen, H Xiong, C H Q Ding, J Chen. Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 2012, 42(1): 218–233 https://doi.org/10.1109/TSMCB.2011.2163711
28	I Porteous, A U Asuncion, M Welling. Bayesian matrix factorization with side information and dirichlet process mixtures. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 563–568
29	L M De Campos, J M Fernández-Luna, J F Huete, A Miguel, M A Rueda-Morales. Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks. International Journal of Approximate Reasoning, 2010, 51(7): 785–799 https://doi.org/10.1016/j.ijar.2010.04.001
30	S Park, Y D Kim, S Choi. Hierarchical bayesian matrix factorization with side information. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 1593–1599
31	D Agarwal, B C Chen. Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 19–28 https://doi.org/10.1145/1557019.1557029
32	K Yoshii, M Goto, K Komatani, T Ogata, G H Okuno. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Transactions on Audio, Speech, and Language Processing, 2008, 16(2): 435–447 https://doi.org/10.1109/TASL.2007.911503
33	Q Li, H S Myaeng, M B Kim. A probabilistic music recommender considering user opinions and audio features. Information Processing and Management, 2007, 43(2): 473–487 https://doi.org/10.1016/j.ipm.2006.07.005
34	R Cheng, B Y Tang. A music recommendation system based on acoustic features and user personalities. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2016, 203–213 https://doi.org/10.1007/978-3-319-42996-0_17
35	K Benzi, V Kalofolias, X Bresson, P Vandergheynst. Song recommendation with non-negative matrix factorization and graph total variation. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2016, 2439–2443 https://doi.org/10.1109/ICASSP.2016.7472115
36	N P Krivitsky, S M Handcock, E A Raftery, D P Hoff. Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social Networks, 2009, 31(3): 204–213 https://doi.org/10.1016/j.socnet.2009.04.001
37	F Kooti, K Lerman, M L Aiello, M Grbovic, N Djuric, V Radosavljevic. Portrait of an online shopper: understanding and predicting consumer behavior. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016, 205–214 https://doi.org/10.1145/2835776.2835831
38	C Spearman. The proof and measurement of association between two things. The American Journal of Psychology, 1904, 15(1): 72–101 https://doi.org/10.2307/1412159
39	N Zumel, J Mount, J Porzak. Practical Data Science with R. Manning Publications Co., 2014
40	Q Liu, Y Ge, Z M Li, E H Chen, H Xiong. Personalized travel package recommendation. In: Proceedings of the 11th IEEE International Conference on Data Mining. 2011, 407–416 https://doi.org/10.1109/ICDM.2011.118
41	Q Liu, E H Chen, H Xiong, Y Ge, Z M Li, X Wu. A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 278–293 https://doi.org/10.1109/TKDE.2012.233
42	R Forsati, M Mahdavi, M Shamsfard, M Sarwat. Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Transactions on Information Systems, 2014, 32(4): 17 https://doi.org/10.1145/2641564
43	D Wackerly, W Mendenhall, L R Scheaffer. Mathematical Statistics with Applications. Nelson Education, 2007

[1]

Article highlights

Download

[1]	Tao LIAN, Lin DU, Mingfu ZHAO, Chaoran CUI, Zhumin CHEN, Jun MA. Evaluating and improving the interpretability of item embeddings using item-tag relevance information[J]. Front. Comput. Sci., 2020, 14(3): 143603-.
[2]	Liang SUN, Hongwei GE, Wenjing KANG. Non-negative matrix factorization based modeling and training algorithm for multi-label learning[J]. Front. Comput. Sci., 2019, 13(6): 1243-1254.
[3]	Dakun LIU,Xiaoyang TAN. Max-margin non-negative matrix factorization with flexible spatial constraints based on factor analysis[J]. Front. Comput. Sci., 2016, 10(2): 302-316.
[4]	Richong ZHANG,Han BAO,Hailong SUN,Yanghao WANG,Xudong LIU. Recommender systems based on ranking performance optimization[J]. Front. Comput. Sci., 2016, 10(2): 270-280.
[5]	Jiliang TANG, Xufei WANG, Huiji GAO, Xia HU, Huan LIU. Enriching short text representation in microblog for clustering[J]. Front Comput Sci, 2012, 6(1): 88-101.

Viewed

Full text

Abstract

Cited

Shared

Discussed