Leveraging proficiency and preference for online Karaoke recommendation

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

References

[1] Backstrom L, Leskovec J. Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the 4th ACMInternational Conference onWeb Search and DataMining. 2011, 635–644

[2] McFee B, Barrington L, Lanckriet G. Learning content similarity for music recommendation. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(8): 2207–2218

[3] Liu N H. Comparison of content-based music recommendation using different distance estimation methods. Applied Intelligence, 2013, 38(2): 160–174

[4] Bogdanov D, Haro M, Fuhrmann F, Gómez E, Herrera P. Contentbased music recommendation based on user preference examples. Copyright Information, 2010, 33: 1–6

[5] Guan C, Fu Y J, Lu X J, Xiong H, Chen E H, Liu Y L. Vocal competence based karaoke recommendation: a maximum-margin joint model. In: Proceedings of 2016 SIAM International Conference on Data Mining. 2016, 135–143

[6] Soleymani M, Aljanaki A, Wiering F, Veltkamp R C. Content-based music recommendation using underlying music preference structure. In: Proceedings of 2015 IEEE International Conference on Multimedia and Expo. 2015, 1–6

[7] Guo C. 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

[8] Celma O. The exploit-explore dilemma in music recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems. 2016, 377

[9] Song T H, Peng Z H, Wang S Z, Fu W J, Hong X G, Yu P S. Based cross-domain recommendation through joint tensor factorization. In: Proceedings of International Conference on Database Systems for Advanced Applications. 2017, 525–540

[10] Wu X, Liu Q, Chen E H, He L, Lv J S, Cao C, Hu G P. Personalized next-song recommendation in online karaokes. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 137–140

[11] Jin R, Chai Y J, Si L. 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

[12] Herlocker J L, Konstan J A, Terveen L G, Riedl J T. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 2004, 22(1): 5–53

[13] Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. 2001, 285–295

[14] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30–37

[15] Sindhwani V, Bucak S, Hu J, Mojsilovic A. A family of non-negative matrix factorizations for one-class collaborative filtering problems. In: Proceedings of the ACM Conference on Recommender Systems. 2009

[16] Liu J T, Wu C H, Liu W Y. Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decision Support Systems, 2013, 55(3): 838–850

[17] Ma H, Yang H X, Lyu R M, King I. Sorec:social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008, 931–940

[18] Ma H, King I, Lyu R M. 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

[19] Jamali M, Ester M. 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

[20] Yang X W, Steck H, Liu Y. 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

[21] Li H, Wu D M, Tang W B, Mamoulis N. Overlapping community regularization for rating prediction in social recommender systems. In: Proceedings of the 9th ACM Conference on Recommender Systems. 2015, 27–34

[22] Eck D, Lamere P, Bertin-Mahieux T, Green S. Automatic generation of social tags for music recommendation. In: Proceedings of the 20th International Conference on Neural Information Processing Systems. 2008, 385–392

[23] Yan Y, Liu T L, Wang Z Y. A music recommendation algorithm based on hybrid collaborative filtering technique. In: Proceedings of Chinese National Conference on Social Media Processing. 2015, 233–240

[24] Hofmann T. 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

[25] Si L, Jin R. Flexible mixture model for collaborative filtering. In: Proceedings of the 20th International Conference on Machine Learning. 2003, 704–711

[26] Liu N N, Yang Q. 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

[27] Liu Q, Chen E H, Xiong H, Ding C H Q, Chen J. Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 2012, 42(1): 218–233

[28] Porteous I, Asuncion A U, Welling M. Bayesian matrix factorization with side information and dirichlet process mixtures. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010, 563–568

[29] De Campos L M, Fernández-Luna J M, Huete J F, Miguel A, Rueda-Morales M A. Combining content-based and collaborative recommendations: a hybrid approach based on bayesian networks. International Journal of Approximate Reasoning, 2010, 51(7): 785–799

[30] Park S, Kim Y D, Choi S. Hierarchical bayesian matrix factorization with side information. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 1593–1599

[31] Agarwal D, Chen B C. Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009, 19–28

[32] Yoshii K, Goto M, Komatani K, Ogata T, Okuno G H. 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

[33] Li Q, Myaeng H S, Kim M B. A probabilistic music recommender considering user opinions and audio features. Information Processing and Management, 2007, 43(2): 473–487

[34] Cheng R, Tang B Y. 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

[35] Benzi K, Kalofolias V, Bresson X, Vandergheynst P. 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

[36] Krivitsky N P, Handcock S M, Raftery E A, Hoff D P. Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social Networks, 2009, 31(3): 204–213

[37] Kooti F, Lerman K, Aiello M L, Grbovic M, Djuric N, Radosavljevic V. 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

[38] Spearman C. The proof and measurement of association between two things. The American Journal of Psychology, 1904, 15(1): 72–101

[39] Zumel N, Mount J, Porzak J. Practical Data Science with R. Manning Publications Co., 2014

[40] Liu Q, Ge Y, Li Z M, Chen E H, Xiong H. Personalized travel package recommendation. In: Proceedings of the 11th IEEE International Conference on Data Mining. 2011, 407–416

[41] Liu Q, Chen E H, Xiong H, Ge Y, Li Z M, Wu X. A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 278–293

[42] Forsati R, Mahdavi M, Shamsfard M, Sarwat M. Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Transactions on Information Systems, 2014, 32(4): 17

[43] Wackerly D, Mendenhall W, Scheaffer L R. Mathematical Statistics with Applications. Nelson Education, 2007

RIGHTS & PERMISSIONS

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