Applying Machine Learning Techniques to Automatically Process and Match Candidates Applications to Job Descriptions

TAS(Techno Aero Services Inc.)是一家专门从事航空、工程和技术的加拿大招聘机构。在TAS，候选人目前通过网络界面向数据库提交简历。这些简历随后由招聘人员手动处理，然后合适的候选人才能匹配到某个职位。通过这种繁琐的手工过程，优秀的潜在候选人往往会在成堆的求职者中迷失方向。该项目旨在利用文本挖掘和机器学习技术，从潜在候选人提交的简历和LinkIn、Twitter和Facebook等网站上的公共个人资料中自动收集有关潜在候选人的信息，以便为招聘人员提供候选人优势和劣势的总体情况。应聘者的技术能力和沟通能力都将被分析和总结。我们的目标是为招聘人员提供他们需要的信息，以判断候选人对关键技能的掌握程度以及他适应公司文化的能力。为了实现这一目标，我们将利用大数据处理基础设施，包括通过Google Tensor Flow等框架进行机器学习，从文本数据中自动提取关于潜在候选人的有用信息。深度学习方法已经被证明是语音、文本和图像识别等各种问题中模式识别和分类的有力工具。我们希望它在自动检测从简历和网络上收集的关于潜在候选人的数据中关于他们的有用信号方面也能取得良好的表现。

TAS (Techno Aero Services inc.) is a Canadian recruitment agency specialized in aeronautics, engineering, andtechnology. At TAS, candidates currently submit resumes to a database through a web interface. These resumesare then manually processed by recruiters before a suitable candidate is matched to a position. Through thislaborious manual process, great prospective candidates often get lost in the piles. This project aims to leveragetext mining and machine learning techniques to automatically collect information about prospective candidatesfrom their submitted resume and public profiles on websites like Linked In, Twitter, and Facebook to providerecruiters with an overall picture of a candidate's strengths and weaknesses. Both the technical andcommunication skills of the candidates will be analyzed and summarized. The goal is to provide recruiters withthe information they need to judge a candidate on both his mastery of key skills and his ability to fit into thecompany culture.To achieve this goal, we will leverage big data processing infrastructure, including machine learning throughframeworks like Google Tensorflow, to automatically extract useful information from textual data aboutprospective candidates. Deep learning approaches have proved to be powerful tools for patterns recognition andclassification in diverse problems, such as speech, text, and image recognition. We expect it to also achieve agood performance in automatically detecting useful signal about prospective candidates from data collectedabout them from their resume and the Web.