Deep learning and word embeddings for HR processes

HireGround是一家加拿大公司，专门从事将简历与招聘广告相匹配的工具，从而减轻了招聘人员的负担，否则他们无法处理单个招聘广告的大量申请人。他们的客户来自加拿大经济的不同和大部分。我们致力于改善招聘流程的多个方面，从而做出更好的招聘决策，让雇主和员工更快乐。对求职者的简历进行匹配和排名是人工智能领域的一个长期挑战。一个趋势是使用定制的推荐系统，它通过矢量化职位描述和简历来工作，使资格，经验和证书与矢量的维度相对应。然后，通过比较（工作和简历的）向量来完成匹配。因此，推荐系统不能轻松地处理快速变化的部门，如IT，其中新的技能出现的所有时间（需要所有的向量被重新计算每一次）。信息检索（IR）技术，以克服这个问题存在。例如，如果简历和职位描述有相当多的重叠术语，语言建模可以用于针对单个招聘广告（查询）对简历（文档）进行排名。在这个项目中，我们将使用一种新的算法来扩展基于IR的解决方案，该算法基于深度学习技术产生的单词嵌入来对给定职位描述的简历进行评分。这样的嵌入是上下文中单词的向量表示。例如，因为“聚类”和“分类”这两个词经常被用来描述机器学习任务，所以它们会有类似的嵌入。因此，人们可以将提到其中一个术语的简历与提到另一个术语的职位描述相匹配，即使是近似的。类似地，合并术语“machine”和“learning”的词嵌入将产生一个类似于“clustering”和“classification”的嵌入的向量，允许在先前方法失败的情况下进行匹配。

HireGround is a Canadian company that specializes in tools for matching resumes to job ads, thus relieving the burden on recruiters who could not otherwise process the large volume of applicants for a single job ad. They have clients from different and large segments of the Canadian economy. We seek to enhance multiple aspects of the process, leading to better recruitment decisions and happier employers and employees. Matching and ranking resumes of candidates for job ads is a longstanding challenge in AI. One trend is to use custom-built recommender systems, which work by vectorizing job descriptions and resumes in such a way that qualifications, experience, and certifications correspond to the dimensions of the vectors. Then, matching is done by comparing vectors (of jobs and resumes). As such, recommender systems cannot easily deal with fast-changing sectors such as IT, where new skills appear all the time (requiring all vectors to be recomputed each time).Information Retrieval (IR) techniques to overcome this problem exist. For example, language modeling can be used to rank resumes (documents) against a single job ad (query), if the resumes and job descriptions have considerable overlapping terminology. In this project we will extend IR-based solutions with a new algorithms for scoring resumes given job descriptions based on word embeddings produced by Deep Learning techniques. Such embeddings are vectorial representations of the words within context. For example, because the words "clustering" and "classification" are used frequently to describe machine learning tasks, they would have similar embeddings. Thus, one can match a resume that mentions one of the terms to a job description mentioning the other, even if approximately. Similarly, merging the word embeddings for the terms "machine" and "learning" will result in a vector that is similar to the embeddings of "clustering" and "classification", allowing for a match where the previous methods would fail.