A New Feature Extraction Approach to Extract Area of Expertise from Resumes to Augment the Hiring Process

Text Feature extraction is a process of detecting and discovering promising data from a large unordered textual data set. The main objective of the feature extraction process is to unearth the promising data and transmit them in to acceptable format to help in decision making. With the ever evolving digital technology number of resumes posted everyday seeking for a job increases steeply and this voluminous data intricate the recruitment firms to identify the right candidate for the right job. The main objective of this paper is to deals with a new feature extraction approach using ranking based frequent text occurrences to extract promising texts from the resume dataset and reduces the hiring agencies manual work considerably, reduces the dimensionality of the data to a larger extent and thereby reduces the running or execution time and memory footprints required largely when compared with the existing approaches

Enhancing Productivity of Recruitment Process Using Data Mining & Text Mining Tools

Digital communication has significantly reduced the time it takes to send a résumé, but the recruiter’s work has become more complicated because with this technological advancement they get more résumés for each job opening. It becomes almost impossible to physically scan each résumé that meets their organization’s job requirement. The filtering and search techniques provide hundreds of résumés that can fulfill the desired criteria. Most approaches focus on either parsing the résumé to get information or propose some filtering methods. Moreover, résumés vary in format and style, making it difficult to maintain a structural repository which would contain all the necessary information. The goal of this project is to examine and propose an approach which would consider the skill sets from the potential résumés, along with expertise domains like related work experience and education, to score the selected “relevant résumé.” This approach aims at highlighting the most important and relevant résumés, thus saving an enormous amount of time and effort that is required fo

CurEval - Curriculum Evaluation

Efficiently screening and evaluating curricula in recruitment processes is a critical task that often requires substantial time and effort from Human Resources professionals. This work presents CurEval, an algorithm developed to automate the evaluation and screening of curricula based on vacancy requirements. The algorithm utilizes a predefined set of keywords and a CSV file format for input, facilitating easy data structuring and processing. To validate the algorithm’s performance and address privacy concerns, synthetic curricula were generated using templates with slight variations in personal data. The algorithm’s results were compared with evaluations made by a Human Resources collaborator and external paid recruitment platforms. The study’s findings indicate that CurEval effectively filters out irrelevant curricula, reducing the screening workload for HR professionals. The algorithm aligns with human evaluations, ensuring accurate classification of curricula according to vacancy requirements. Additionally, bias analysis revealed no evidence of discriminatory bias in the algorithm or human evaluations in the sample data. Further improvements for CurEval include expanding the list of keywords, incorporating natural language processing techniques, and integrating machine learning to enhance accuracy and adaptability. Real-time data integration, feedback loops with HR professionals, and integration with Applicant Tracking Systems are suggested to streamline the recruitment process. Multi-lingual support, performance metrics, and ongoing ethical considerations are also essential for refining and maintaining the algorithm’s effectiveness and fairness. CurEval offers promising potential to revolutionize the curricula evaluation process, enabling faster and more efficient screening while ensuring fairness and equal opportunity. Future work should focus on enhancing the algorithm’s capabilities, addressing biases, and continuously validating and improving its performance through collaboration and feedback from HR professionals.A automação da análise e classificação de currículos tem sido alvo de estudo e destaque nas últimas décadas, guiado pela evolução e aperfeiçoamento dos algoritmos de Inteligência Artificial e da Machine Learning. Nesta dissertação vai ser abordado o processo de análise e classificação destes assim como as questões éticas e bias associados ao processo que advém da natureza humana e das vivências individuais do recrutador. De forma a se evitar que estes ocorram durante o processo de recrutamento foi desenvolvido um algoritmo de análise e classificação dos currículos de acordo com a vaga em questão. Para além deste serão criados standards para a classificação e análise dos currículos, independentemente da sua origem e dos formatos. O algoritmo utiliza um conjunto pré-definido de palavras-chave e um formato de arquivo CSV para entrada, facilitando a estruturação e processamento dos dados. Para validar o desempenho do algoritmo e abordar preocupações de privacidade, currículos sintéticos foram gerados usando modelos com pequenas variações nos dados pessoais. Os resultados do algoritmo foram comparados com avaliações feitas por um colaborador de Recursos Humanos e plataformas externas de recrutamento pagas. Os resultados do estudo indicam que o CurEval filtra efetivamente currículos irrelevantes, reduzindo a carga de trabalho de triagem para os profissionais de RH. Este está alinhado com as avaliações humanas, garantindo a classificação precisa dos currículos de acordo com os requisitos das vagas. Além disso, a análise de viés discriminatórios revelou que não há evidências da existência dos mesmos no algoritmo ou nas avaliações humanas para a amostragem. Melhorias futuras para o CurEval incluem a expansão da lista de palavras-chave, a incorporação de técnicas de processamento de linguagem natural e a integração de Machine Learning para aprimorar a precisão e adaptabilidade. Integração de dados em tempo real, ciclos de feedback com profissionais de RH e integração com Sistemas de Acompanhamento de Candidatos são sugeridos para otimizar o processo de recrutamento. Suporte a múltiplos idiomas, métricas de desempenho e considerações éticas contínuas são essenciais para refinar e manter a eficácia e equidade do algoritmo

Human Resources Recommender system based on discrete variables

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing and Understanding has become one of the most exciting and challenging fields in the area of Artificial Intelligence and Machine Learning. With the rapidly changing business environment and surroundings, the importance of having the data transformed in such a way that makes it easy to interpret is the greatest competitive advantage a company can have. Having said this, the purpose of this thesis dissertation is to implement a recommender system for the Human Resources department in a company that will aid the decision-making process of filling a specific job position with the right candidate. The recommender system fill be fed with applicants, each being represented by their skills, and will produce a subset of most adequate candidates given a job position. This work uses StarSpace, a novelty neural embedding model, whose aim is to represent entities in a common vectorial space and further perform similarity measures amongst them

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

Enhancing Job Recommendation through LLM-based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms, as it can enhance users' satisfaction and the platforms' profitability. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness. With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful capabilities of text processing and reasoning, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for the models in generating high-quality resumes. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.Comment: 13 pages, 6 figures, 3 table

ONTOLOGY BASED TECHNICAL SKILL SIMILARITY

Online job boards have become a major platform for technical talent procurement and job search. These job portals have given rise to challenging matching and search problems. The core matching or search happens between technical skills of the job requirements and the candidate\u27s profile or keywords. The extensive list of technical skills and its polyonymous nature makes it less effective to perform a direct keyword matching. This results in substandard job matching or search results which misses out a closely matching candidate on account of it not having the exact skills. It is important to use a semantic similarity measure between skills to improve the relevance of the results. This paper proposes a semantic similarity measure between technical skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score. Feature based ontology similarity measures are used to derive a similarity score between two skills. The ontology also helps in resolving a base skill from its multiple representations. The paper discusses implementation of custom ontology, similarity measuring system and performance of the system in comparing technical skills. The proposed approach performs better than the Resumatcher system in finding the similarity between skills. Keywords

Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end to end Ability aware Person Job Fit Neural Network model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word level semantic representation for both job requirements and job seekers' experiences based on Recurrent Neural Network. Along this line, four hierarchical ability aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large scale real world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines.Comment: This is an extended version of our SIGIR18 pape

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates