Application of Collaborative Filtering Algorithm in Human Resource Emergency Scheduling System

The complexity and uncertainty of enterprise operations make human resource emergency dispatch systems play an increasingly important role in ensuring that organizations can respond quickly to emergencies. An effective scheduling system can optimize employee allocation, improve the efficiency and effectiveness of emergency response. This study explored the application of collaborative filtering algorithms in human resource emergency scheduling systems, aiming to improve scheduling performance by intelligently matching employee skills with urgent task needs. This study constructed a dataset that includes employee skills, historical scheduling records, and task requirements. Through a series of comparative experiments, this study evaluated the performance of collaborative filtering algorithms, content-based recommendation algorithms, and rule-based scheduling algorithms in three key indicators: matching accuracy, scheduling response time, and employee satisfaction. The conclusion showed that the collaborative filtering algorithm had achieved significant results in the experiment. The accuracy of the collaborative filtering algorithm was as high as 97%, with a minimum response time of 99ms and a maximum response time of 148ms. Compared to the response times of content-based 234ms and rule-based 289ms, the collaborative filtering algorithm demonstrated faster processing speed. The results of the employee satisfaction survey also indicated that the satisfaction rating of employee scheduling under the collaborative filtering algorithm could reach up to 100, maintaining an overall high level, significantly higher than the other two algorithms. These data strongly demonstrate the superiority of collaborative filtering algorithms in human resource emergency scheduling systems.