arXiv：cs.CL· Ege S\"ualp, Elena Senger, Toine Bogers, Barbara Plank·· 10 小时前
职业派生技能为何无益：技能增强型职业路径预测的局限
When Occupation-derived Skills Don't Help: On the Limits of Skill-Augmented Career Path Prediction
AI 导读
研究评估了将技能表示引入职业路径预测（CPP）模型的多种策略，包括分数融合与神经网络架构，并在预测技能与 ESCO 真实标注两种条件下测试。在两个基准数据集上，这些方法均未显著超越强文本基线，即使使用真实技能，提升也有限且不稳定。作者归因于文本与技能表示高度冗余、ESCO 分类体系判别力有限及技能嵌入表征坍缩。
正文
Abstract:Skills are widely assumed to play a central role in career transitions, motivating their use in career path prediction (CPP) systems. In this work, we critically examine whether explicit skill representations provide complementary signal beyond text-based job representations. We evaluate multiple strategies for incorporating skills into CPP models, including score fusion and neural architectures, under two conditions: predicted skills and ground-truth ESCO annotations. Surprisingly, across two benchmark datasets, none of these settings yield substantial improvements over strong text-only baselines. Even when using ground-truth skills, gains remain marginal and inconsistent. Through detailed analysis, we identify key factors underlying this result: high redundancy between textual and skill representations, limited discriminative power of ESCO's taxonomy, and representational collapse in skill embeddings. Furthermore, we find that skill prediction difficulty correlates with overall sample difficulty, limiting the potential for complementary signals. Our findings challenge the common assumption that explicit skill modeling inherently benefits career prediction and suggest that dense text representations already capture most skill-relevant information.
| Comments: | RecSys in HR |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20186 [cs.CL] |
| (or arXiv:2610.20186v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20186 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Elena Senger [view email]
[v1]
Wed, 14 Oct 2026 13:37:47 UTC (3,643 KB)
来源：arXiv：cs.CL · arxiv.org