Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering - Haiwen Xu; Yifan Kong; Hong Huang; Aimin Liang; Yunxiang Zhao (Aerospace)
This paper proposes a new model of collaborative filtering that introduces a user difference factor to address the issue of less pronounced similarity performance in traditional algorithms. The algorithm is applied to the recommendation of pilots’ core competency behavior indicators to support pilots’ daily training arrangements, improve their core competencies, and ensure civil aviation flight safety. Firstly, based on traditional collaborative filtering methods, a user difference factor is introduced to improve the Pearson similarity calculation model. Secondly, the advantages and disadvantages of collaborative filtering recommendation models were evaluated using various methods such as average absolute error, accuracy, recall, and diversity. Finally, the new model is applied to the recommendation of PLM core competency behavior indicators, providing a recommendation list of different pilot behavior indicators to support their rehabilitation or enhanced training plans and arrangements. The calculation results show that the new model of collaborative filtering demonstrates better advantages, not only reducing the MAE value, but also improving the accuracy, recall, and diversity of the calculation results, providing effective guidance and theoretical support for pilots’ flight training and safe flight.