Personalized Sports Health Recommendation System Assisted by Q-Learning Algorithm

Abstract

In response to the current problem of single sports plan and lack of long-term motivation in recommendation systems, a more intelligent personalized sports health recommendation system was designed by introducing Q-Learning (Quality Learning) algorithm. Firstly, user sports health data was collected, and the user model was constructed to track user sport preferences and historical behavior. Secondly, the sports environment was defined, including different types of sports activities, venues, and weather. Then, the reward function was formulated to reward and punish users based on their sports activities and goals, in order to maximize long-term health benefits. Finally, the Q-Learning algorithm was implemented to continuously iteratively learn and optimize user recommendation models to provide the best personalized sports recommendations. For personalized accuracy, indicators such as precision, recall, F1 value, MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) were used to evaluate, while the system’s participation in sports, user satisfaction, long-term incentive effects, and overall health improvement were collected. The results showed that the average precision of the recommendation system on 10 different datasets was 88%, and the average AUC (Area Under Curve) was 96%, which was 6.7% higher than the SVD (Singular Value Decomposition) algorithm. The user’s sports persistence rate was improved by 25%, and the health score was improved by about 13.3%. These data not only reflect the superior performance of the recommendation system but also highlight its positive impact on long-term user motivation and overall health levels. The results indicate that the proposed personalized exercise health recommendation system, assisted by the Q-Learning algorithm, has significantly improved accuracy. Moreover, it offers users more intelligent and personalized exercise suggestions, effectively increasing long-term participation in physical activities and overall health levels.

Keywords:

• Sports health system
• Q-Learning algorithm
• personalized recommendations
• user modeling
• long-term motivation

Disclosure statement

The authors declare that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Additional information

Funding

No funding were used to support this study.

Notes on contributors

Yang Yang

Yang Yang was born in Meishan, Sichuan province, China in 1980. He received the doctor’s degree from Xinan University, China. Now, he is working in College of Physical Education, Chengdu Sport University. His research interests include health education, national fitness, physical education training.

Yuanji Zhao

Yuanji Zhao was born in Tongxu County, Henan Province in 1972. He is currently pursuing a PhD at Chengdu Sport University. He is currently working at the School of Physical Education at Wuhan Sports University. Research directions include physical education, national fitness, and public services.