Learning to match applicants to job profiles

学习将申请人与职位简介相匹配

• 批准号: 521887-2017
• 负责人: Bruce, Neil
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640128
• 关键词: Learning; match; applicants; job; profiles

The proposed research activities will help to improve capabilities in matching prospective job applicants tojobs posted by hiring managers seeking suitable candidates. This is a research endeavor grounded in machinelearning, data science and neural networks towards extracting maximum value from data, and providing bestpossible matches, and outcomes in hiring.The proposed research will help with decision making based on large and heterogeneous data, also providingnew capabilities, and scalability in new directions with potential for exponential growth. Data consideredranges from text based candidate descriptions to more targeted measures that inform on quality of fit in fillingjobs.Modern advances in AI and deep learning will be applied, alongside careful principles of system design toprovide optimal matches, and successful outcomes post-hire.

拟议的研究活动将有助于提高能力，使潜在的工作申请人与招聘管理人员为寻找合适候选人而发布的职位相匹配。这是一项基于机器学习、数据科学和神经网络的研究奋进，旨在从数据中提取最大价值，并提供最佳匹配和招聘结果。拟议的研究将有助于基于大型异构数据的决策，还提供新的功能，以及在新方向上的可扩展性，具有指数增长的潜力。数据考虑从基于文本的候选人描述到更有针对性的衡量标准，以告知填补工作的适合性。人工智能和深度学习的现代进步将被应用，以及系统设计的谨慎原则，以提供最佳匹配，并在招聘后取得成功。