Recruiting PE Teachers Based on Regional Socio-Economic Status Evaluation and Recommendation Algorithm
Keywords:Bayesian networks, PE teachers, recommendation algorithms, social–economic status, workforce development
The most important step in creating a teaching force for physical education (PE) is finding enough qualified teachers. In order to better absorb the PE teaching talents who are more suitable for the job requirements, the ability variables of sports talents, the expected regional social and economic status, and historical data are considered, the intelligent matching of talents and positions is made, and the Bayesian variational network recommendation model considering the needs is constructed. According to the experimental findings, this model’s highest recommendation accuracy in the normal scenario is 0.5888 and its maximum recommendation accuracy in the training and test sets is roughly 0.6 and 0.68. The model has good convergence and high accuracy of recommendation, which is conducive to matching PE teaching talents and teaching positions, providing job seekers with positions that meet their needs, providing teaching talents to meet the requirements, and creating a team of PE teachers that match people and posts.
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