Home > Archive > 2020 > Volume 10 Number 1 (Jan. 2020) >
IJMLC 2020 Vol.10(1): 201-206 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.1.920

Research on Automatic Generation of Table Tennis Technique and Tactics Collection Template

Jing Sun, Haochen Luo, and Huiqun Zhao

Abstract—In the professional table tennis team, the content of the game is summarized after each contest. The summary process needs to set up technical and tactical acquisition templates according to the coach's intention, and then perform technical and tactical data collection and analysis according to the template. However, due to the randomness of the template, manual design of the template requires a lot of manpower and time, making the technical and tactical collection efficiency very low. In order to improve the acquisition speed, this paper analyzes the recommendation algorithm based on collaborative filtering and its implementation process. Combined with the process of table tennis technology and tactics collection and analysis, this paper proposes a solution to automatically generate table tennis tactics acquisition template. Experiments show that this method reduces the time required by the method of manually designing the template by 50% and improves the collection efficiency.

Index Terms—Table tennis skills and tactics collection, recommendation system, coll, orative filtering, automated generation.

The authors are with Computer School, North China University of Technology, Beijing, 100144, China (e-mail: sunjing8248@163.com, 923816437@.com, zhaohq6625@sina.com).

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Cite: Jing Sun, Haochen Luo, and Huiqun Zhao, "Research on Automatic Generation of Table Tennis Technique and Tactics Collection Template," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 201-206, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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