Precise Recommendation Algorithm for Online Sports Video Teaching Resources




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


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.


Ahmadian Yazdi, H., Seyyed Mahdavi Chabok, S. J., & Kheirabadi, M. (2022). Dynamic educational recommender system based on improved recurrent neural networks using attention technique. Applied Artificial Intelligence, 36(1), 2005298.

Lalitha T B., Sreeja P S. (2020). Personalised Self-Directed Learning Recommendation System. Procedia Computer Science, 171(1), 583-592.

Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2020). A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies, 25(4), 2635-2664.

Liu, S. (2019) Introduction of Key Problems in Long-Distance Learning and Training, Mobile Networks and Applications, 24(1): 1-4.

Peng, P., Fu, W. (2022) A Pattern Recognition Method of Personalized Adaptive Learning in Online Education, Mobile Networks & Applications, 27(3):1186-1198

Komar, J., Potdevin, F., Chollet, D., & Seifert, L. (2019). Between exploitation and exploration of motor behaviours: unpacking the constraints-led approach to foster nonlinear learning in physical education. Physical Education and Sport Pedagogy, 24(2), 133-145.

Li, H., Li, H., Zhang, S., Zhong, Z., & Cheng, J. (2019). Intelligent learning system based on personalized recommendation technology. Neural Computing and Applications, 31(9), 4455-4462.

Yang, Y., Zhong, Y., Woźniak, M. (2021). Improvement of adaptive learning service recommendation algorithm based on big data. Mobile Networks and Applications, 26(5): 2176-2187.

Li, Y., Zhu, J. & Fu, W. (2022) Intelligent Privacy Protection of End User in Long Distance Education, Mobile Networks & Applications, 27(3): 1162-1173

Cohenmiller, A. S. & Miller, M. V. (2019). Resources for online teaching in the social and natural sciences: a multistage search and classification of open video repositories. College Teaching, 67(2), 1-7.

Geng, C., Zhang, J. & Guan, L. (2021). A recommendation method of teaching resources based on similarity and als. Journal of Physics: Conference Series, 1865(4), 042043.

Chen, H. , Yin, C. , Li, R. , Rong, W. , Xiong, Z. & David, B. (2020). Enhanced learning resource recommendation based on online learning style model. Tsinghua Science and Technology, 25(3), 348-356.

Liu G., Zhou B., Huang Y., et al. (2021). Video Image Scaling Technology Based on Adaptive Interpolation Algorithm and Tts FPGA Implementation[J]. Computer Standards & Interfaces, 76(1), 103516.

Sderman P., Grinnemo K J., Hidell M., et al. (2021). A Comparative Analysis of Buffer Management Algorithms for Delay Tolerant Wireless Sensor Networks[J]. IEEE Sensors Journal, 7(2), 9612-9619.

Soderstrom D., Luza L M., Kettunen H., et al. (2021). Electron-Induced Upsets and Stuck Bits in SDRAMs in the Jovian Environment[J]. IEEE Transactions on Nuclear Science, 2021, 68(5):716-723.

Liu, X. (2019). A collaborative filtering recommendation algorithm based on the influence sets of e-learning group’s behavior. Cluster Computing, 22(2), 2823-2833.

Liu, S., Xu, X., Zhang, Y., et al. (2022) A Reliable Sample Selection Strategy for Weakly-supervised Visual Tracking, IEEE Transactions on Reliability, online first, doi: 10.1109/TR.2022.316234

Xanat, V. M. & Toshimasa, Y. (2019). A video recommendation system for complex topic learning based on a sustainable design approach. Vietnam Journal of Computer Science, 06(3), 329-342.

Chen K S, Yu C M. Fuzzy test model for performance evaluation matrix of service operating systems[J]. Computers & Industrial Engineering, 2020, 140(1):106240.

Wang, S., Liu, X., Liu, S., et al. (2022) Human Short-Long Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities. IEEE Internet of Things Journal, 9(10): 7128-7139.




How to Cite

Zhu X, Zhang Z. Precise Recommendation Algorithm for Online Sports Video Teaching Resources. EAI Endorsed Scal Inf Syst [Internet]. 2022 Oct. 26 [cited 2024 Jul. 25];10(2):e11. Available from: