Semantic search using deep networks****

Hire Ground是一家领先的申请人跟踪软件（ATS）服务提供商，为其客户的人力资源招聘和保留工作提供支持。ATS的一个关键组成部分是能够比较候选人是否适合给定的职位描述，这是招聘过程中最耗时和劳动密集型的任务，因为它取决于候选人的数量，通常可以达到数百人。由于简历中使用的语言多样性和细微差别，将简历与职位描述进行计算匹配是一项重大挑战。**我们建议采用我们实验室在过去几年中开发的新的语义方法，根据申请人的职位适合性对给定的招聘广告的简历进行自动排名。基于简历和职位描述的术语嵌入表示，根据职位描述和每份简历之间的语义距离来估计专业知识的相关性。我们将评估深度网络的不同变体，如卷积和循环神经网络，以及词嵌入的不同变体。提出的方法的一个关键组成部分是人在循环：人类专家（公司的人力资源专家）将提供反馈，以允许用最少的训练数据训练深度网络模型。这项研究将推动语义搜索技术的发展，并帮助行业合作伙伴在其市场中获得竞争优势。* * * *

Hire Ground is a leading service provider of Applicant Tracking Software (ATS) supporting its clients in their human resource hiring and retention efforts. A key component of ATS is the ability to compare candidates regarding their suitability for a given job description, the most time-consuming and labour intensive task of the hiring process because it depends on the number of candidates, which can often be in the hundreds. Matching resumes with job descriptions computationally is a major challenge due to language variety and nuance used in resumes. **We propose to adapt novel semantic methods developed in our lab in the last several years for automatically ranking resumes of applicants to a given job ad on their suitability for the position. The relevance of expertise will be estimated based on the semantic distance between the job description and each resume based on a term embedding representation of resume and job description. We will evaluate different variants of deep networks, such as Convolutional and Recurrent Neural Networks, and different variants of word embeddings. A key component of the proposed methodology is the human-in-the-loop: the human expert (human resources specialist of a company) will provide feedback to allow the training of the deep network models with minimal training data. The research will advance the state of the art in semantic search and help the industry partner achieve a competitive advantage in their market. ****