arXiv cs.AI· Jung Ho Choi, Mario Milone·· 1 天前AI 评分31
用就业存续衡量员工-企业匹配质量：双塔神经网络嵌入与匹配质量指标
Human Capital in Context: What Employment Survival Reveals About Worker-Firm Match
AI 导读
研究者提出一种员工-企业匹配质量指标，用双塔神经网络将员工职业经历与企业特征、业务描述嵌入同一空间，以两向量余弦相似度作为匹配质量。该指标在覆盖 1.01 亿员工、840 万企业的超十亿人-期数据上估计，匹配质量提高 0.1 可使离职几率降低 48%，约四分之一的匹配质量方差为员工-企业配对特有。匹配更好的员工收入更高，员工匹配得分更高的企业在随后五年增长更快、利润更高、专利更多且更有价值。
正文 · 原文
Abstract:There is no best worker in general, only a best worker for a particular firm. We develop a measure of worker-firm match quality that treats fit as a property of the pair, learned from the survival of employment relationships. A two-tower neural network embeds workers, from their career histories and current positions, and firms, from their characteristics and business descriptions, into a common space, and match quality is the cosine similarity of the two vectors. The measure takes its functional form from the skill-weights model of firm-specific human capital, and its only estimated objective, a separation hazard, from the view of the job match as an experience good. Estimated on more than a billion person-periods covering 101 million workers and 8.4 million firms, a 0.1 increase in match quality lowers the odds of separation by 48%. About a quarter of the variance of match quality is specific to the worker-firm pair, which worker-, firm-, and occupation-level measures set to zero by construction. Workers earn more when better matched, especially early in a relationship, and firms whose workforces score higher grow faster, become more profitable, and are issued more, and more valuable, patents over the next five years than their industry peers. Because it scores pairs that never formed, the measure also predicts where movers go, and acquirers record a larger share of the price as goodwill when the target's workers would fit the acquirer better than they fit the target.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); General Economics (econ.GN) |
| Cite as: | arXiv:2611.16058 [cs.LG] |
| (or arXiv:2611.16058v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16058 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jungho Choi [view email]
[v1]
Wed, 11 Nov 2026 22:13:36 UTC (64 KB)
来源：arXiv cs.AI · arxiv.org