Towards a New Job Offers Recommendation System Based on the Candidate Resume

Abstract

The recruitment web platforms are a solution that has solved several problems, such as eliminating the filing of paper files to organizations, reducing recruitment time, etc. In recent years and especially with the pandemic, the job offers increased across the web with a great diversity of professional platforms and networks offering these offers. This increase in the number of job openings and the number of web platforms presents difficulties for candidates. The candidates must register and create an account on each recruiting platform. This implies a considerable waste of time for candidates to manage their accounts and to find suitable jobs according to the candidate's profile on each platform. Most recommendation systems are oriented towards the jobs platform and not towards the candidate, and depend heavily on the jobs platform. So candidates can apply on job offers specific to their specialties and skills, we propose a recommendation system for the online job offers to allow the classification of job offers to correspond to the profiles (e-portfolio) of graduates or candidates according to their skills. Our research work is carried out from extracting to calculating similarity and ranking passing by specific processing for the preparation of our data set and the classification of job offers and e-portfolios according to the skill specialty.

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).…”

ResumeCraft: A Machine Learning-powered Web Platform for Resume Building

“…Particularly beneficial for recent graduates or those lacking resume expertise or time, this platform streamlines resume creation with its user-friendly interface. Mgarbi et al (2023) [16] designed an application centered on crafting visually appealing resumes using diverse templates and fonts. Users can also include links to their online profiles, such as LinkedIn and GitHub.…”

“…Víctor Fresno et al (2016) [2] discussed the importance of document representation in document clustering tasks, introducing a fuzzy term weighting approach that capitalizes on HTML structure for enhanced clustering performance. Jaiswal et al (2024) [15] noted the exponential growth of information on the World Wide Web and the inconvenience of current human intervention requirements for leveraging this wealth of information. Zied Jaoua et al (2012) [3] proposed an algorithm for robust reception of compressed HTML files over noisy mobile radio channels, assuming standard compliance of both source encoders and transmission systems.…”