A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory

WU Yun; LIN Jian; MA Yanlong

doi:10.23919/cje.2021.00.172

Volume 32 Issue 6

Nov. 2023

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WU Yun, LIN Jian, MA Yanlong, “A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory,” Chinese Journal of Electronics, vol. 32, no. 6, pp. 1319-1328, 2023, doi: 10.23919/cje.2021.00.172

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WU Yun, LIN Jian, MA Yanlong, “A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory,” Chinese Journal of Electronics, vol. 32, no. 6, pp. 1319-1328, 2023, doi: 10.23919/cje.2021.00.172

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A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory

doi: 10.23919/cje.2021.00.172

WU Yun^{1, 2
,},
LIN Jian^{1, 2
,},
MA Yanlong¹

1.
The State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
2.
College of Computer Science and Technology, Guizhou University, Guiyang 550025, China

Funds: This work was supported by the National Natural Science Foundation of China (62266011) and the Science and Technology Foundation of Guizhou Province (ZK[2022]119)

More Information

Author Bio:
Yun WU was born in 1973. He received the Ph.D. degree from Guizhou University, Guizhou, China, in 2009. Now he is an Associate Professor, a Senior Member of CCF, the Director of the Institute of Computer Simulation of Guizhou University, an Evaluation Expert of Guizhou University, and the Academic Leader of the Big Data Technology and Internet Application. His research interests include game theory, recommender system, big data and its application. (Email: wuyun_v@126.com)

Jian LIN (corresponding author) was born in 1994. He is an M.S. student in the College of Computer Science and Technology, Guizhou University. His current research interests include recommender system and data mining. (Email: gs.linj19@gzu.edu.cn)

Yanlong MA was born in 1991. He received the M.S. degree in the College of Computer Science and Technology, Guizhou University, China, in 2016. His research interests include distributed computing and recommender system
Received Date: 2021-05-15
Accepted Date: 2022-07-13

Available Online: 2022-10-20

Publish Date: 2023-11-05

Abstract

Abstract

Music recommendation algorithms, from the perspective of real-time, can be classified into two categories: offline recommendation algorithms and online recommendation algorithms. To improve music recommendation accuracy, especially for the new music (users have no historic listening records on it), and real-time recommendation ability, and solve the interest drift problem simultaneously, we propose a hybrid music recommendation model based on personalized measurement and game theory. This model can be separated into two parts: an offline recommendation part (OFFLRP) and an online recommendation part (ONLRP). In the offline part, we emphasize users personalization. We introduce two metrics named user pursue-novelty degree (UPND) and music popularity (MP) to improve the traditional items-based collaborative filtering algorithm. In the online part, we try to solve the interest drift problem, which is a thorny problem in the offline part. We propose a novel online recommendation algorithm based on game theory. Experiments verify that the hybrid music recommendation model has higher new music recommendation accuracy, decent dynamical personalized recommendation ability, and real-time recommendation capability, and can substantially mitigate the problem of interest drift.
- Music recommendation,
- Personalization,
- Game theory,
- Interest drift,
- Hybrid music recommendation model

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References(28)

References

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