发表机构
ManpowerGroup Services India Pvt. Ltd.; Indian Institute of Technology, Hyderabad（万宝盛华服务印度私人有限公司; 印度理工学院海得拉巴分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在混合检索流水线中比较稠密嵌入模型（EmbeddingGemma及MNRL微调版与MPNet），用于职位-候选人匹配，通过批量评估和明确指标范围提供可复现的模型选择依据。
AI 中文摘要
本文针对高吞吐量招聘流程中的语义候选人与职位匹配，对稠密嵌入模型进行了比较评估。传入的职位描述通过基于LLM的解析转换为结构化英文搜索文本和特定语言关键词，候选人档案则被索引为语义增强的简历表示。我们在统一的混合检索流水线中评估了EmbeddingGemma（基础版）与使用缓存多负样本排序损失（MNRL）微调的EmbeddingGemma，该流水线通过倒数排名融合（RRF）融合向量相似性和全文相关性，并在通过已部署的职位-候选人匹配评分流水线评分的批量比较评估数据集上，将两者与MPNet模型进行基准比较。我们进一步以数学细节记录了模型开发过程中考虑的更广泛的对比微调目标集（包括AnglE/CoSENT风格的精炼），以及保留仅缓存MNRL自适应作为首选配置的经验依据。为支持可复现的模型选择，我们定义了一个更广泛的评估框架，包括在精确混合检索协议下的标准信息检索指标（Recall@K、平均倒数排名、nDCG）；本文报告的评估所使用的指标是微调收敛诊断和使用已部署的AI-Match评分及独立的LLM-as-a-Judge相关性评分进行的批量比较评估，我们明确说明了这一范围，而非暗示完整框架已被测量。本文解决了通用嵌入基准与企业职位-候选人匹配约束之间的差距，为在现实职位-候选人检索条件下比较嵌入策略提供了结构化基础。
英文摘要
This paper presents a comparative evaluation of dense embedding models for semantic candidate-job matching in high-volume staffing workflows. Incoming job descriptions are converted into structured English search text and language-specific keywords through LLM-based parsing, and candidate profiles are indexed as semantically enriched resume representations. We evaluate EmbeddingGemma (base) against EmbeddingGemma fine-tuned with Cached Multiple Negatives Ranking Loss (MNRL) within a unified hybrid retrieval pipeline that fuses vector similarity and full-text relevance via reciprocal rank fusion (RRF), and benchmark both against the MPNet model on a batch comparative evaluation dataset scored through the deployed job-candidate matching scoring pipeline. We further document, with mathematical detail, the broader set of contrastive fine-tuning objectives considered during model development (including AnglE/CoSENT-style refinement) and the empirical rationale for retaining Cached-MNRL-only adaptation as the preferred configuration. To support reproducible model selection, we define a broader evaluation framework comprising standard information retrieval metrics (Recall@K, mean reciprocal rank, nDCG) under the exact hybrid-retrieval protocol; the metrics used for the evaluation reported in this paper are fine-tuning convergence diagnostics and a batch comparative evaluation using the deployed AI-Match score and an independent LLM-as-a-Judge relevance score, and we state this scope explicitly rather than implying the full framework was measured. The paper addresses the gap between general-purpose embedding benchmarks and enterprise job-candidate matching constraints, providing a structured basis for comparing embedding strategies under realistic job-candidate retrieval conditions.
