Personalized Music Recommendation Algorithm TFPMF

Authors

Xining Ye, College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;
Wang Meng, College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;

Abstract

Abstract: In recent years, the personalized context-aware recommendation is the rub and hotness in the research of recommendation system, and the data sparseness is the main problem faced by the current recommendation systems. In the setting of music recommendation, the representing method of varieties of situational information is improved. A model of TFPMF is proposed, which combines the model of RR-PMF with the tensor decomposition. TFPMF is optimized by alternative least squares (ALS). By the simulation experiments in the last.fm dataset, we got the TOP-N recommended list through the simulation program. The simulation results show that the proposed algorithm has great advantages in the evaluation index of Precision, Recall and NDCG, and the algorithm can effectively alleviate the data sparsity problem.

Recommended Citation

Ye, Xining and Meng, Wang (2019) "Personalized Music Recommendation Algorithm TFPMF," Journal of System Simulation: Vol. 31: Iss. 7, Article 17.
DOI: 10.16182/j.issn1004731x.joss.17-0256
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss7/17

First Page

1397

Revised Date

2017-08-23

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0256

Last Page

1407

CLC

TP391

Recommended Citation

Ye Xining, Wang Meng. Personalized Music Recommendation Algorithm TFPMF[J]. Journal of System Simulation, 2019, 31(7): 1397-1407.

DOI

10.16182/j.issn1004731x.joss.17-0256