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

作     者：WU Yun LIN Jian MA Yanlong WU Yun;LIN Jian;MA Yanlong

作者机构：The State Key Laboratory of Public Big Data Guizhou University College of Computer Science and Technology Guizhou University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2023年第32卷第6期

页      面：1319-1328页

核心收录：

学科分类：13[艺术学] 1302[艺术学-音乐与舞蹈学] 0808[工学-电气工程] 08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (62266011) the Science and Technology Foundation of Guizhou Province (ZK119) 

主　　题：Music recommendation Personalization Game theory Interest drift Hybrid music recommendation model 

摘      要：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.