This study introduces a benchmarking methodology designed to evaluate the performance of AI-driven recruitment sourcing tools. We created and utilized a dataset to perform a comparative analysis of search results generated by leading AI-based solutions, LinkedIn Recruiter, and our proprietary system, Pearch.ai. Human experts assessed the relevance of the returned candidates, and an Elo rating system was applied to quantitatively measure each tool's comparative performance. Our findings indicate that AI-driven recruitment sourcing tools consistently outperform LinkedIn Recruiter in candidate relevance, with Pearch.ai achieving the highest performance scores. Furthermore, we found a strong alignment between AI-based evaluations and human judgments, highlighting the potential for advanced AI technologies to substantially enhance talent acquisition effectiveness. Code and supporting data are publicly available at https://github.com/vslaykovsky/ai-sourcing-benchmark

翻译：本研究提出了一种用于评估AI驱动招聘寻源工具性能的基准测试方法。我们创建并利用了一个数据集，对主流AI解决方案、LinkedIn Recruiter以及我们自主研发的Pearch.ai系统生成的搜索结果进行了比较分析。由人类专家对返回候选人的相关性进行评估，并采用Elo评分系统对各工具的相对性能进行量化测量。研究结果表明，在候选人相关性方面，AI驱动的招聘寻源工具持续优于LinkedIn Recruiter，其中Pearch.ai获得了最高的性能评分。此外，我们发现基于AI的评估与人类判断具有高度一致性，这凸显了先进AI技术在显著提升人才获取效能方面的潜力。代码及相关数据已在https://github.com/vslaykovsky/ai-sourcing-benchmark 公开。