A music recommendation algorithm based on clustering and latent factor model

Yingjie Jin; Chunyan Han

doi:10.1051/matecconf/202030903009

All issues

Volume 309 (2020)

MATEC Web Conf., 309 (2020) 03009

References

Open Access

Issue		MATEC Web Conf. Volume 309, 2020 2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)


Article Number		03009
Number of page(s)		9
Section		Smart Algorithms and Recognition
DOI		https://doi.org/10.1051/matecconf/202030903009
Published online		04 March 2020

Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using markov chain monte carlo[C]//International Conference on Machine Learning. ACM, 2008. [Google Scholar]
Yin J, Wang ZS, Li Q, Su WJ. Personalized recommendation based on large-scale implicit feedback. Ruan Jian Xue Bao/Journal of Software, 2014,25(9):1953–1966(in Chinese). http://Www.jos.org.cn/1000-9825/4648.htm [Google Scholar]
Yu Chunhua, Liu Xuejun, Li Bin, et al. Context-aware recommendation of social information fusion in implicit feedback scenarios[J]. Computer Science, 2016, 43(6): 248–253. [Google Scholar]
Yu Shuai, Lin Xuanxiong, Qiu Yuanyuan. A Word Vector Music Recommendation Model for Large-Scale Implicit Feedback. Computer Systems Applications, 2017, 26(11):28–35. http://www.csa.org.cn/1003-3254/6049.html [Google Scholar]
Wang Zhisheng, Li Qi, Wang Jing, et al. Real-time personalized recommendation based on implicit user feedback data stream [J]. Chinese Journal of Computers, 2016(1): 52–64. [Google Scholar]
He Ming, Sun Wang, Xiao Run, et al. A Collaborative Filtering Recommendation Algorithm Based on Fusion Clustering and User Interest Preference [J]. Computer Science, 2017(S2): 401–406. [Google Scholar]
Li Tao, Fu Ding. Automated Implicit Grading Music Dual Recommendation System Based on Collaborative Filtering Algorithm [J]. Journal of Computer Measurement and Control, 2018, 26(11): 177–181. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using markov chain monte carlo[C]//International Conference on Machine Learning. ACM, 2008. [Google Scholar]

[2] Yin J, Wang ZS, Li Q, Su WJ. Personalized recommendation based on large-scale implicit feedback. Ruan Jian Xue Bao/Journal of Software, 2014,25(9):1953–1966(in Chinese). http://Www.jos.org.cn/1000-9825/4648.htm [Google Scholar]

[3] Yu Chunhua, Liu Xuejun, Li Bin, et al. Context-aware recommendation of social information fusion in implicit feedback scenarios[J]. Computer Science, 2016, 43(6): 248–253. [Google Scholar]

[4] Yu Shuai, Lin Xuanxiong, Qiu Yuanyuan. A Word Vector Music Recommendation Model for Large-Scale Implicit Feedback. Computer Systems Applications, 2017, 26(11):28–35. http://www.csa.org.cn/1003-3254/6049.html [Google Scholar]

[5] Wang Zhisheng, Li Qi, Wang Jing, et al. Real-time personalized recommendation based on implicit user feedback data stream [J]. Chinese Journal of Computers, 2016(1): 52–64. [Google Scholar]

[6] He Ming, Sun Wang, Xiao Run, et al. A Collaborative Filtering Recommendation Algorithm Based on Fusion Clustering and User Interest Preference [J]. Computer Science, 2017(S2): 401–406. [Google Scholar]

[7] Li Tao, Fu Ding. Automated Implicit Grading Music Dual Recommendation System Based on Collaborative Filtering Algorithm [J]. Journal of Computer Measurement and Control, 2018, 26(11): 177–181. [Google Scholar]