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

TAS （Techno Aero Services inc.）是加拿大一家专业从事航空、工程和技术的招聘机构。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工处理，然后将合适的候选人与职位相匹配。通过这种费力的手工流程，优秀的潜在候选人往往会在成堆的简历中迷失方向。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Linked In、Twitter和Facebook等网站上的公开资料中自动收集信息，为招聘人员提供候选人优缺点的总体情况。对候选人的技术和沟通能力进行分析和总结。这样做的目的是为招聘人员提供他们需要的信息，以便根据候选人对关键技能的掌握程度和融入公司文化的能力来判断候选人。为了实现这一目标，我们将利用大数据处理基础设施，包括通过谷歌Tensorflow等框架进行机器学习，从潜在候选人的文本数据中自动提取有用的信息。深度学习方法已被证明是在语音、文本和图像识别等各种问题中进行模式识别和分类的强大工具。我们希望它也能从应聘者的简历和网络上收集到的数据中自动检测出潜在求职者的有用信号。

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.