微粒度语义增强图形神经网络模型,用于人与工作的合适性
Xia Xue1, Jingwen Wang1, Bo Ma1
1Maths and Information Technology School, Yuncheng University, Yuncheng 044000, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
这项研究引入了一个新的框架,用于在线招聘中改善人与工作的合适性. 细粒度语义增强图形神经网络 (FSEGNN-PJF) 通过分析文本结构和减少噪声来提高匹配精度.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 人与计算机的交互
背景情况:
- 智能招聘系统依赖于准确的人与工作的匹配.
- 目前的方法使用粗的语义分析,忽略了简历/职位描述中的文本结构和噪音.
研究的目的:
- 提出一个新的细粒度语义增强图形神经网络,以适应人与工作 (FSEGNN-PJF).
- 通过解决当前招聘方法的局限性,提高人才获取的准确性.
主要方法:
- 使用单词共发生 (分点相互信息,滑动窗口) 构建图形拓.
- 使用图表注意力网络来学习图表结构语义.
- 使用微分变压器和自我注意力来进行简历和工作要求的语义编码.
- 实施精细的语义匹配策略,增强功能融合.
主要成果:
- 证明了FSEGNN-PJF框架的有效性和稳定性.
- 与现有方法相比,在人与职位相适应性评估方面取得了更高的表现.
- 成功地减轻了文本噪音,并专注于关键特征以更好地匹配.
结论:
- 在智能招聘中,FSEGNN-PJF框架显著推进了人与工作的合适性分析.
- 精细的语义分析和图形神经网络为人才获取提供了更强大的方法.
- 这种方法为优化在线招聘平台提供了一个有希望的方向.
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