Abstract
Through analyzing users’ listening records, personalized music recommendation can not only help users find interesting music, but also help related enterprises improve user loyalty. This paper proposes an improved music recommendation method based on bipartite graph link prediction with homogeneous nodes similarity. Firstly, users’ music preference relations are expressed as positive and negative binary preference relations by the Complex Representation-based Link Prediction (CORLP) method, which improves the limitation of traditional recommendation method that can only represent unary preference relations. Secondly, the new method improves the CORLP method by attribute extraction and similarity calculation of homogeneous nodes including user nodes and music nodes. Thirdly, a new dataset based on the practical data from Shrimp Music Community is collected for facilitating the music recommendation task. The first-class music genres and second-class music genres of users are extracted by web crawling technology to calculate the similarity between user nodes. The rhythm and tempo are extracted by open source software to calculate the similarity between music nodes. Finally, the Top-N experiment is used to prove the performance of the proposed method compared with CORLP. In addition, the results reveal several new findings. Firstly, performance is significantly improved when the homogeneous nodes similarity is taken into account. Secondly, recommendation method with user nodes similarity shows a better performance compared with recommendation method with music nodes similarity.
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References
Åman P, Liikkanen L (2017) Interacting with context factors in music recommendation and discovery. Int J Hum-Comput Int 33(3):165–179
Andjelkovic I, Parra D, O'Donovan J (2016) Moodplay: interactive mood-based music discovery and recommendation. Conference on User Modeling Adaptation and Personalization pp. 275-279.
Celma O (2010) Music recommendation and discovery. In: Music recommendation. Springer, Berlin, pp 43–85
Chang H, Huang S, Wu J (2017) A personalized music recommendation system based on electroencephalography feedback. Multimed Tools Appl 76(19):19523–19542
Chen J, Ying P, Zou M (2018) Improving music recommendation by incorporating social influence. Multimed Tools Appl 78(3):2667–2687
Cheng Z, Shen J (2016) On effective location-aware music recommendation. ACM T Inform Syst 34(2):13
Deng S, Wang D, Li X, Xu G (2015) Exploring user emotion in microblogs for music recommendation. Expert Syst Appl 42(23):9284–9293
Dias R, Fonseca M (2013) Improving music recommendation in session-based collaborative filtering by using temporal context. International conference on tools with artificial intelligence pp. 783-788.
Dolatkia I, Azimzadeh F (2016) Music recommendation system based on the continuous combination of contextual information. International Conference on Web Research pp. 108-114. IEEE.
Flexer A, Stevens J (2018) Mutual proximity graphs for improved reachability in music recommendation. J New Music Res 47(1):17–28
Gong N, Talwalkar A, Mackey L, Huang L, Shin E et al (2014) Joint link prediction and attribute inference using a social-attribute network. ACM T Intel Syst Tec 5(2):27
Gossi D, Gunes M (2016) Lyric-based music recommendation. In: Complex networks VII. Springer, Cham, pp 301–310
Guo C (2016) Feature generation and selection on the heterogeneous graph for music recommendation. ACM International Conference on Web Search and Data Mining pp. 715-715.
Katarya R, Verma O (2018) Efficient music recommender system using context graph and particle swarm. Multimed Tools Appl 77(2):2673–2687
Li Y, Luo P, Fan Z, Chen K, Liu J (2017) A utility-based link prediction method in social networks. Eur J Oper Res 260(2):693–705
Lichtenwalter R, Chawla N (2011) Lpmade: link prediction made easy. J Mach Learn Res 12:2489–2492
Lin K, Xu Z, Liu J, Wu Q, Chen Y (2016) Personalized music recommendation algorithm based on tag information. International Conference on Software Engineering and Service Science pp. 229-232.
Lin Q, Niu Y, Zhu Y, Lu H, Mushonga K, Niu Z (2018) Heterogeneous knowledge-based attentive neural networks for short-term music recommendations. IEEE Access 6:58990–59000
Mao K, Chen G, Hu Y, Zhang L (2016) Music recommendation using graph based quality model. Signal Process 120:806–813
Melville P, Sindhwani V (2017) Recommender systems. In: Encyclopedia of machine learning and data mining. Springer, Berlin, pp 1056–1066
Oramas S, Ostuni V, Noia T, Serra X, Sciascio E (2017) Sound and music recommendation with knowledge graphs. Acm T Intel Syst Tec 8(2):21
Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, Boston, pp 1–34
Sánchez-Moreno D, González A, Vicente M, Batista V, García M (2016) A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert Syst Appl 66:234–244
Schedl M (2016). The lfm-1b dataset for music retrieval and recommendation. International Conference on Multimedia Retrieval pp. 103-110.
Sunitha M, Adilakshmi T (2018) Music recommendation system with user-based and item-based collaborative filtering technique. In: Networking communication and data knowledge engineering. Springer, Singapore, pp 267–278
Wang H, Hu W, Qiu Z, Du B (2017) Nodes' evolution diversity and link prediction in social networks. IEEE T Knowl Data En 29(10):2263–2274
Wang D, Deng S, Xu G (2018) Sequence-based context-aware music recommendation. Inform Retrieval J 21(2–3):230–252
Xie F, Chen Z, Shang J, Feng X, Li J (2015) A link prediction approach for item recommendation with complex number. Knowl-Based Syst 81:148–158
Yan Y, Liu T, Wang Z (2015) A music recommendation algorithm based on hybrid collaborative filtering technique. Chinese National Conference on Social Media Processing pp. 233-240.
Yang J, Chae W, Kim S, Choi H (2016) Emotion-aware music recommendation. International Conference of Design, User Experience, and Usability pp. 110-121.
Zhu Y, Lu L (2012) Evaluation metrics for recommender systems. Journal of University of Electronic Science and Technology of China 41(2):163–175
Acknowledgments
This work is supported by National Natural Science Foundation of China (Grant No. 91546201, 71471169 and 71071151). The authors are very grateful for the valuable comments and suggestions from anonymous reviewers and editor of the journal, which significantly improved the quality of the paper.
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Zhang, L., Zhao, M. & Zhao, D. Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation. Multimed Tools Appl 79, 13197–13215 (2020). https://doi.org/10.1007/s11042-019-08451-x
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DOI: https://doi.org/10.1007/s11042-019-08451-x