Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering
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 pilot...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/1/9 |
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| _version_ | 1832589451301224448 |
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| author | Haiwen Xu Yifan Kong Hong Huang Aimin Liang Yunxiang Zhao |
| author_facet | Haiwen Xu Yifan Kong Hong Huang Aimin Liang Yunxiang Zhao |
| author_sort | Haiwen Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0e92e1b7e0054e72abbd96d80018063a |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-0e92e1b7e0054e72abbd96d80018063a2025-01-24T13:15:25ZengMDPI AGAerospace2226-43102024-12-01121910.3390/aerospace12010009Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative FilteringHaiwen Xu0Yifan Kong1Hong Huang2Aimin Liang3Yunxiang Zhao4Faculty of Science, Civil Aviation Flight University of China, Deyang 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618307, ChinaCivil Aviation Flight University of China Guanghan Flight College, Deyang 618307, ChinaCivil Aviation Flight University of China Mianyang Flight College, Mianyang 621000, ChinaFaculty of Science, Civil Aviation Flight University of China, Deyang 618307, ChinaThis 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.https://www.mdpi.com/2226-4310/12/1/9collaborative filteringPLMcore competenciesobservable behavior |
| spellingShingle | Haiwen Xu Yifan Kong Hong Huang Aimin Liang Yunxiang Zhao Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering Aerospace collaborative filtering PLM core competencies observable behavior |
| title | Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering |
| title_full | Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering |
| title_fullStr | Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering |
| title_full_unstemmed | Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering |
| title_short | Research on Recommendation of Core Competencies and Behavioral Indicators of Pilots Based on Collaborative Filtering |
| title_sort | research on recommendation of core competencies and behavioral indicators of pilots based on collaborative filtering |
| topic | collaborative filtering PLM core competencies observable behavior |
| url | https://www.mdpi.com/2226-4310/12/1/9 |
| work_keys_str_mv | AT haiwenxu researchonrecommendationofcorecompetenciesandbehavioralindicatorsofpilotsbasedoncollaborativefiltering AT yifankong researchonrecommendationofcorecompetenciesandbehavioralindicatorsofpilotsbasedoncollaborativefiltering AT honghuang researchonrecommendationofcorecompetenciesandbehavioralindicatorsofpilotsbasedoncollaborativefiltering AT aiminliang researchonrecommendationofcorecompetenciesandbehavioralindicatorsofpilotsbasedoncollaborativefiltering AT yunxiangzhao researchonrecommendationofcorecompetenciesandbehavioralindicatorsofpilotsbasedoncollaborativefiltering |