在基于能力的框架内,用于预测项目经理绩效的多模式推系统
Imene Jemal1, Wilfried Armand Naoussi Sijou1, Belkacem Chikhaoui1
1Applied Artificial Intelligence Institute, TELUQ University, Montreal, QC, Canada.
Frontiers in big data
|May 24, 2024
概括
本研究引入了一种新的推者系统方法,用于自动预测项目经理能力分数. 基于内容的过显示了高精度,改善了性能评估,并有效地解决了冷启动问题.
科学领域:
- 计算社会科学 计算社会科学
- 人力资源 技术 技术 人力资源
- 绩效管理系统 绩效管理系统
背景情况:
- 项目经理的传统能力评估是手动的,耗时的,容易产生评估者偏见.
- 准确的绩效评估对于专业发展和组织成功至关重要.
- 现有的方法在评分各种能力方面缺乏效率和一致性.
研究的目的:
- 开发和评估用于预测项目经理能力分数的自动化方法.
- 在这种情况下,比较各种推者系统技术的有效性.
- 为了应对能力评估中冷启动的挑战.
主要方法:
- 人口,个人资料和历史评估数据的数据融合.
- 自然语言处理 (NLP) 用于文本数据的预处理.
- 基于内容,人口,协作和混合过推系统的比较.
主要成果:
- 基于内容的过实现了一般能力评分的81.03%精度.
- 人口过显示新项目经理的准确率为54.05% (冷启动).
- 基于内容的过显示了新能力的85.79%精度 (冷启动).
结论:
- 推系统为自动化能力评估提供了可行和高效的解决方案.
- 基于内容的过对于一般和冷启动能力预测都特别有效.
- 这种方法可以显著简化项目经理的绩效评估.
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