This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61% is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job T2. Our best models using section encoding and multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms.
Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning
IT University of Copenhagen（哥本哈根信息技术大学）；LMU Munich（慕尼黑大学）
技能分类（SC）是从职位发布中对工作能力进行分类的任务。这项工作是第一次在SC适用于丹麦的职位空缺数据。我们发布了第一个丹麦招聘数据集：Kompetencer（en：competences），注释了嵌套的能力跨度。为了改进粗粒度注释，我们利用欧洲技能、能力、资格和职业（The European Skills，Competences，Qualifications and Occupational）（1999; le Vrang et al.，2014）分类API通过远程监督获得细粒度标签。我们研究了两种设置：zero-shot和Few-Shot分类设置。我们微调基于英语的模型和RemBERT（Chung等人，2020年），并将其与丹麦语模型进行比较。我们的研究结果表明，RemBERT显着优于所有其他模型在zero-shot和Few-Shot设置。
JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes
AIDA-IDLab, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium（AIDA-ID实验室，电子与信息系统系，根特大学，根特，比利时）；AIDA-IDLab, Department of Electronics（AIDA-ID实验室，电子系）；Information Systems, Ghent University, Ghent, Belgium（信息系统，根特大学，根特，比利时）
A Large-scale Industrial and Professional Occupation Dataset
Singapore University of Technology and Design（新加坡科技设计大学）；National University of Singapore（新加坡国立大学）
人们对利用职业数据采掘和分析越来越感兴趣。在当今的就业市场中，职业数据采掘和分析的重要性越来越大，因为它使公司能够预测员工流动率，建立职业轨迹模型，筛选简历并执行其他人力资源任务。促进这些任务的一个关键要求是需要一个与职业有关的数据集。然而，大多数研究使用专有数据集或不公开数据集，从而阻碍了这一领域的发展。为了解决这个问题，我们提出了工业和专业职业数据集（IPOD），其中包括属于56 k LinkedIn用户的192 k职位。除了公开IPOD，我们还：（i）手动注释每个职位及其相关的资历级别，工作领域和位置;以及（ii）提供职位嵌入并讨论各种用例。该数据集可在https://github.com/junhua/ipod上公开获得。
KARRIEREWEGE: A Large Scale Career Path Prediction Dataset
LMU Munich（慕尼黑大学）；Fraunhofer Center for International Management and Knowledge Economy IMW（弗劳恩霍夫国际管理与知识经济中心IMW）；IT University of Copenhagen（哥本哈根信息技术大学）
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
School of Information, Renmin University of China（中国人民大学信息学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京市大数据管理与分析方法重点实验室）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；BOSS Zhipin NLP Center（BOSS直聘自然语言处理中心）
AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening
Imperial College London（伦敦帝国理工学院）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Wedon Education Technologies（韦登教育科技）；Brest Business School（布雷斯特商学院）
MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruiting
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation（下一代智能搜索与推荐工程研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Nanbeige Lab, BOSS Zhipin（Boss Zhipin纳米贝实验室）；King Abdullah University of Science and Technology（卡布斯科学技术大学）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
