|
|
Music Recommendation Method Based on Multi-Source Information Fusion |
Li Danyang,Gan Mingxin() |
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China |
|
|
Abstract [Objective] This paper creates a musical feature system based on multi-source information, aiming to address the cold start issue facing music recommendation and provide personalized services. [Methods] We proposed a two-stage model with multi-source information fused by neural network algorithm. Then, we built the musical feature system and predicted the potential factor vectors of music. Finally, we generated the TopN recommendation list for the users. [Results] We examined our model with the Million Song Dataset. Compared with other models such as CNN, the F1 value was improved by 9.13%, and the RMSE, MAE values were reduced by 8.08% and 3.91%, respectively. [Limitations] Our new method encounters more limits than the end-to-end training ones. And training with the Mel-frequency spectrum demands much more memory. [Conclusions] The proposed model improves the performance of music recommendation services.
|
Received: 04 June 2020
Published: 11 March 2021
|
|
Fund:National Natural Science Foundation of China(71871019);National Natural Science Foundation of China(71471016);National Natural Science Foundation of China(71531013) |
Corresponding Authors:
Gan Mingxin ORCID:0000-0001-8751-0780
E-mail: ganmx@ustb.edu.cn
|
[1] |
Zheng E, Kondo G Y, Zilora S, et al. Tag-aware Dynamic Music Recommendation[J]. Expert Systems with Applications, 2018,106:244-251.
doi: 10.1016/j.eswa.2018.04.014
|
[2] |
Hu Y F, Koren Y, Volinsky C. Collaborative Filtering for Implicit Feedback Datasets[C]//Proceedings of the 8th IEEE International Conference on Data Mining. 2008: 263-272.
|
[3] |
van den Oord A, Dieleman S, Schrauwen B. Deep Content-based Music Recommendation[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013: 2643-2651.
|
[4] |
Bertin-Mahieux T, Ellis P W D, Whitman B, et al. The Million Song Dataset[C]//Proceedings of the 12th International Society for Music Information Retrieval Conference. 2011: 591-596.
|
[5] |
牛滨, 孔令志, 罗森林, 等. 基于MFCC和GMM的个性音乐推荐模型[J]. 北京理工大学学报, 2009,29(4):351-355.
|
[5] |
( Niu Bin, Kong Lingzhi, Luo Senlin, et al. Individuality Music Recommendation Model Based on MFCC and GMM[J]. Transactions of Beijing Institute of Technology, 2009,29(4):351-355.)
|
[6] |
Liu C L, Chen Y C. Background Music Recommendation Based on Latent Factors and Moods[J]. Knowledge-Based Systems, 2018,159:158-170.
doi: 10.1016/j.knosys.2018.07.001
|
[7] |
Li T, Choi M, Fu K M, et al. Music Sequence Prediction with Mixture Hidden Markov Models[C]//Proceedings of 2019 IEEE International Conference on Big Data. Los Angeles, CA, USA, 2018. DOI: 10.1109/BigData47090.2019.9005695.
|
[8] |
Flexer A, Stevens J. Mutual Proximity Graphs for Improved Reachability in Music Recommendation[J]. Journal of New Music Research, 2018,47(1):17-28.
doi: 10.1080/09298215.2017.1354891
pmid: 29348779
|
[9] |
McFee B, Barrington L, Lanckriet G. Learning Content Similarity for Music Recommendation[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012,20(8):2207-2218.
doi: 10.1109/TASL.2012.2199109
|
[10] |
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998,86(11):2278-2324.
doi: 10.1109/5.726791
|
[11] |
Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012: 1097-1105.
|
[12] |
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//Proceedings of International Conference on Learning Representations, 2015.
|
[13] |
Lee J, Lee K, Park J, et al. Deep Content-User Embedding Model for Music Recommendation[OL]. arXiv Preprint, arXiv: 1807. 06786.
|
[14] |
Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Computation, 1997,9(8):1735-1780.
doi: 10.1162/neco.1997.9.8.1735
pmid: 9377276
|
[15] |
Chung J, Gulcehre C, Cho K, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling [OL]. arXiv Preprint, arXiv: 1412. 3555.
|
[16] |
Balakrishnan A, Dixit K. DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity [EB/OL]. [2019-05-16].https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf.
|
[17] |
Sachdeva N, Gupta K, Pudi V. Attentive Neural Architecture Incorporating Song Features for Music Recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems. 2018: 417-421.
|
[18] |
Wang D J, Deng S G, Xu G D. Sequence-Based Context-Aware Music Recommendation[J]. Information Retrieval, 2018,21(2/3):230-252.
doi: 10.1007/s10791-017-9317-7
|
[19] |
Zou W. Design and Application of Incremental Music Recommendation System Based on Slope One Algorithm[J]. Wireless Personal Communications, 2018,102(4):2785-2795.
doi: 10.1007/s11277-018-5303-7
|
[20] |
Deng S G, Wang D J, Li X T, et al. Exploring User Emotion in Microblogs for Music Recommendation[J]. Expert Systems with Applications, 2015,42(23):9284-9293.
doi: 10.1016/j.eswa.2015.08.029
|
[21] |
Ren J, Kauffman R, King D. Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services[C]//Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019: 2679-2688.
|
[22] |
Chen J P, Ying P G, Zou M. Improving Music Recommendation by Incorporating Social Influence[J]. Multimedia Tools and Applications, 2019,78(3):2667-2687.
doi: 10.1007/s11042-018-5745-7
|
[23] |
Cheng Z Y, Shen J L. On Effective Location-Aware Music Recommendation[J]. ACM Transactions on Information Systems, 2016, 34(2): Article 13.
|
[24] |
李洋, 赵鸣, 徐梦瑶, 等. 多源信息融合技术研究综述[J]. 智能计算机与应用, 2019,9(5):186-189.
|
[24] |
( Li Yang, Zhao Ming, Xu Mengyao, et al. A Survey of Research on Multi-Source Information Fusion Technology[J]. Intelligent Computer and Applications, 2019,9(5):186-189.)
|
[25] |
Mikolov T, Chen K, Corrado G, et al. Efficient Estimation of Word Representations in Vector Space[C]//Proceedings of Workshop at International Conference on Learning Representations, 2013.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|