Job-Candidate Matching

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13 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals
Oct 2, 2026Ilya Chekin, Vyacheslav Malyugin, Vladimir Chirkov +1Job-Candidate Matching Interpretable ML
BroutonLab · Curately
Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring
Sep 24, 2026Itai Ashlagi, Ramesh Johari, Jon Kleinberg +1AI and Labor Job-Candidate Matching
Semantic Candidate-Job Matching: A Comparative Evaluation of Dense Embedding Models in Hybrid Retrieval
Sep 20, 2026Sai Yashwant, Siddhartha Jain, Anurag Dubey +2Job-Candidate Matching Text Embedding Models
ManpowerGroup Services India Pvt. Ltd. · Indian Institute of Technology, Hyderabad
When Hiring Becomes Agent-Mediated: Evaluating Access and Recurrence in Two-Agent Résumé Screening
Sep 17, 2026Jian Gao, Hang JiangAI-Assisted Decision Making LLM Agent Evaluation
Northeastern University Boston, MA, USA
Counterfactual Bias Testing for Application Tracking System
Aug 27, 2026Sai Yashwant, Shruti Bansal, Anurag Dubey +4Algorithmic Fairness Algorithmic Bias
ManpowerGroup Services India Pvt. Ltd. · Indian Institute of Technology, Hyderabad
TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management
Jul 22, 2026Luis Gasco, Hermenegildo Fabregat, Laura García-Sardiña +5Job-Candidate Matching
Avature Machine Learning, Spain · NLP & IR Group at UNED, Madrid, Spain
Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management
Jun 30, 2026Luis Gasco, Hermenegildo Fabregat, Laura García-Sardiña +8Job-Candidate Matching
Avature Machine Learning, Spain · TechWolf, Belgium · NLP & IR Group at UNED, Madrid, Spain
ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking
May 10, 2026Xiao Yu, Ruize Xu, Chengyuan Xue +6Reinforcement Learning Learning to Rank
Columbia University · New York, NY, USA · John Hopkins University +6
Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation
Apr 23, 2026Minping Chen, Bing Xu, Zulong Chen +4Data Augmentation Mixture of Experts
HKUST (GZ) · Alibaba Group · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) +3
Mira-Embeddings-V1: Domain-Adapted Semantic Reranking for Recruitment via LLM-Synthesized Data
Apr 20, 2026Zhaohua Liang, Zhilin Wang, Renjie Cao +1Learning to Rank Semantic Search
OpenJobs AI · San Francisico, CA, USA
Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization
Apr 2, 2026Ansel Kaplan Erol, Seohee Yoon, Keenan Hom +1Job-Candidate Matching Recommender Systems
Georgia Institute of Technology, Atlanta, GA, USA
PJB: A Reasoning-Aware Benchmark for Person-Job Retrieval
Mar 18, 2026Guangzhi Wang, Xiaohui Yang, Kai Li +4Job-Candidate Matching Information Retrieval
CareerInternational Research Team
JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
Mar 15, 2026Mayank Vyas, Abhijit Chakraborty, Vivek GuptaJob-Candidate Matching Hybrid IR
Arizona State University

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Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals

Ilya Chekin, Vyacheslav Malyugin, Vladimir Chirkov, Mikhail Yurushkin

BroutonLab · Curately

Job-Candidate Matching Interpretable ML Text Embedding Models

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Matching candidates to vacancies is central to recruitment, and a recruiter needs to see why a candidate fits, not only a single opaque relevance score. We provide this evidence as named, interpretable matching dimensions recruiters can act on - eight in our current deployment. We propose a two-part approach. The first is an LLM-based labeler whose prompts and feature definitions were refined from recruiter feedback while it served as an earlier production matching stage. In the current architecture, it is used only for offline labeling and is not called on online requests. The second is a feature bi-encoder distilled from it: a LoRA-adapted embedding backbone with compact per-dimension heads that runs on CPU and serves all online requests. Both parts keep improving: prompts are revised as feedback arrives, and the bi-encoder is retrained on the updated labels. The model is trained on 168,772 labeled vacancy-resume pairs (17,921 vacancies and 180,030 resumes). Recruiters using the service can confirm or revise surfaced feature predictions. On 927 recruiter-recorded values from this selected production-feedback subset, the deployed student agrees with the recorded decisions in 888 cases (95.79%). This is operational, non-blinded agreement rather than an independent human evaluation.