Precise Recommendation Algorithm for Online Sports Video Teaching Resources

Xu Zhu; Zhaofa Zhang

doi:10.4108/eetsis.v10i1.2578

Authors

Xu Zhu Jiangxi University of Finance and Economics
Zhaofa Zhang Nanchang Hangkong University

DOI:

https://doi.org/10.4108/eetsis.v10i1.2578

Keywords:

sports, online video, teaching resources, precise recommendation, collaborative filtering, algorithm test

Abstract

INTRODUCTION: With the development of the epidemic, online teaching has gradually become a hot topic. However, unlike traditional teaching programs, there are many types of physical education resources, and the recommendation of related content has always been a difficulty in online teaching.

OBJECTIVES: Therefore, this paper designs an accurate recommendation algorithm for online video teaching resources of sports to meet the personalized needs of online learning of sports majors. The data layer of the entire recommendation algorithm stores the video in the database and transmits it to the service processing layer after receiving the data.

METHODS: This study was conducted using techniques from social network analysis. After receiving the data, the data layer of the recommendation algorithm stores the video in the database and transmits it to the business processing layer at the same time. The business processing layer uses the designed collaborative filtering resource recommendation algorithm to formulate recommendation results for different users, and push the recommended results to the user display interface of the user layer.

RESULTS: The test results of the algorithm show that the designed system has a high recommendation success rate, and the system can still maintain stable running performance when the concurrent users are 500. The average precision of resource recommendation of this method is 98.21%, the average recall rate is 98.35%, and the average F1 value is 95.37%.

CONCLUSION: The proposed resource recommendation algorithm realizes accurate recommendation of sports online video teaching resources through efficient recommendation algorithms.

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