Combining Word Embeddings and Deep Neural Networks for Job Offers and Resumes Classification in IT Recruitment Domain

Abstract

Now-a-days, the use of web portals known as job boards for publishing job offers by recruiters has grown considerably. The candidates in their turn, apply to the job positions via the job boards. Since the opportunities are available on a wide range and the job application process is fast and straightforward, the data flow is transformed to large-volume data sets which are hard to handle. Most companies tend to automate the candidate selection process that aims to match the job offers with suitable resumes. In this paper, we propose a supervised learning approach to classify the job offers and CVs shared in the recruitment sites in order to enhance automatic recruitment process. We used natural language processing techniques for job offers and CV preprocessing. Next, we used word embeddings and deep neural networks to train two models, the first one categorizes recruitment documents based on job skills, and the second one predicts the expertise degree class. The experiment results show that our proposal is very efficient.

Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students

“…On the other hand, [34] used combined Word embedding techniques word2Vec and neural network DNN approach for the classification of IT resumes (Web/software development, Network engineering, Embedded software engineering, Testing engineering, Business intelligence, Big data development, Data science, Information systems management, Database administration) and labor market in the Moroccan context. In addition, those approaches can be applied to a recommendation system, where [35] proposed a model as a Recommendation system using data from Moroccan E-recruitment platforms (Rekrute, Emploi.ma, Linkedin, etc) and based on a Classification approach including Weighted Semantic Network and Vector Space Model (VSM).…”

Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles

“…Several researchers have explored word embeddings for resume classification. Some notable and distinctive works in this direction are: -(a) GloVe word embeddings with convolutional neural networks [24] (b) Word2Vec word embeddings with Bidirectional LSTM with attention [25] (c) GloVe word embeddings with deep neural networks [26] (d) GloVe word embeddings with graph neural networks [27]. In [28], Zu and Wang claimed that the combination of Bidirectional LSTM with convolutional neural network and conditional random fields is the best classifier to classify word embedding sequences extracted from text blocks in a resume.…”

Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research

“…Based on collected features, those AIbased algorithms rank the candidates according to their fit for the position (Mujtaba & Mahapatra, 2019). Apart from candidate ranking, classification of résumés is used to categorize candidates according to whether they match certain criteria based on textual properties (Habous & El Habib, 2021). Human recruiters are still needed to monitor the automated process, and they finally decide on whether to hire a candidate (Black & van Esch, 2020;Goretzko & Israel, 2022).…”