Employee Training Method Based on Improved Genetic Algorithm - Ant Colony Optimization and Collaborative Filtering

Abstract: In order to achieve intelligent employee training and improve training quality, an innovative employee training method that integrates collaborative filtering and improved genetic algorithm ant colony optimization algorithm is proposed. This method first adopts collaborative filtering technology, combined with jieba word segmentation and word frequency inverse document frequency algorithm, to intelligently recommend employee training materials, effectively addressing the issues of cold start and data sparsity. Next, an improved genetic algorithm ant colony optimization algorithm is used for intelligent test paper generation to ensure the comprehensiveness and rationality of the assessment paper. The test results show that the average hit rate @5 and average normalized cumulative loss gain @5 of the proposed training material recommendation method are 98.4% and 0.873, respectively, which are better than existing algorithms. In the intelligent paper generation test, the improved genetic algorithm ant colony optimization algorithm showed faster paper generation speed and higher paper quality. When the number of test questions was 300, its fitness value was 0.95, significantly higher than other algorithms. The above outcomes indicate that the proposed method has good effectiveness and practicality in improving the effectiveness of employee training.