1,845 research outputs found
Personality and Education Mining based Job Advisory System
Every job demands an employee with some specific qualities in addition to the basic educational qualification. For example, an introvert person cannot be a good leader despite of a very good academic qualification. Thinking and logical ability is required for a person to be a successful software engineer. So, the aim of this paper is to present a novel approach for advising an ideal job to the job seeker while considering his personality trait and educational qualification both. Very well-known theories of personality like MBTI indicator and OCEAN theory, are used for personality mining. For education mining, score based system is used. The score based system captures the information from attributes like most scoring subject, dream job etc. After personality mining, the resultant values are coalesced with the information extracted from education mining. And finally, the most suited jobs, in terms of personality and educational qualification are recommended to the job seekers. The experiment is conducted on the students who have earned an engineering degree in the field of computer science, information technology and electronics. Nevertheless, the same architecture can easily be extended to other educational degrees also. To the best of the author’s knowledge, this is a first e-job advisory system that recommends the job best suited as per one’s personality using MBTI and OCEAN theory both
Smart job searching system based on information retrieval techniques and similarity of fuzzy parameterized sets
Job searching for the proper vacancy among several choices is one of the most important decision-making problems. The necessity of dealing with uncertainty in such real-world problems has been a long-term research challenge which has originated from different methodologies and theories. The main contribution of this work is to match the applicant curriculum vitae (CV) with the best available job opportunities based on certain criteria. The proposed job searching system (JSS) implements a series of approaches which can be broken down into segmentation, tokenization, part of speech, gazetteer, and fuzzy inference to extract and arrange the required data from the job announcements and CV. Moreover, this study designs a fuzzy parameterized structure to store such data as well as a measuring tool to calculate the degree of similarity between the job requirements and the applicant’s CV. In addition, this system analyses the computed similarity scores in order to get the optimal job opportunities for the job seeker in descending order. The performance evaluation of the proposed system shows high recall and precision percentages for the matching process. The results also confirm the viability of the JSS approach in handling the fuzziness that is associated with the problem of job searching
Digital transformation in recruitment : best practices in the Portuguese market
The “war for talent” is leading organizations to focus on recruitment process agility and employer branding as a way to successfully recruit the best individuals. For this, companies are increasingly turning to implementing new technological developments in their recruitment and selection processes. This dissertation focuses on identifying the impact these technologies can cause, and what the best practices are for implementing them into recruitment processes, from the perspective of the internal recruiters. For this, a qualitative analysis was conducted through researching existing literature and conducting semi-structured interviews with recruiters. The results show that the implementation of automatic CV screening, asynchronous video interviewing and assessment games have a positive impact on recruitment processes, namely through increased accuracy of assessment, shortening the process duration, increasing diversity among recruits and enabling higher candidate pools. Companies are also focusing on increased software integration and gathering of recruitment metrics for continuous process improvement. For the successful implementation of these changes, recruiters see organizational culture as a key factor.A “guerra pelo talento” está a levar a que as organizações se foquem na agilidade do processo de recrutamento e em employer branding para recrutar os melhores candidatos. Para tal, as empresas estão a recorrer cada vez mais à implementação de novas tecnologias nos seus processos de recrutamento e seleção. Esta dissertação foca-se em identificar o impacto que estas tecnologias podem causar, e quais as melhores práticas para a sua integração em processos de recrutamento da perspetiva dos recrutadores internos. Para tal, uma análise qualitativa foi conduzida através da investigação da literatura existente e da condução de entrevistas semiestruturadas com recrutadores. Os resultados mostram que a implementação de triagem de CV automática, entrevistas em vídeo assíncronas e jogos de avaliação têm um impacto positivo em processos de recrutamento, nomeadamente através de um aumento na precisão das avaliações, encurtamento da duração do processo, aumento da diversidade entre recrutados e permitir um volume de candidaturas maior. As empresas também se estão a focar em aumentar a integração de software e a coleta de métricas de recrutamento para melhoria contínua dos processos. Para a implementação bem-sucedida destas alterações, os recrutadores vêm a cultura organizacional como um fator chave
Improving the matching of registered unemployed to job offers through machine learning algorithms
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDue to the existence of a double-sided asymmetric information problem on the labour market
characterized by a mutual lack of trust by employers and unemployed people, not enough job matches
are facilitated by public employment services (PES), which seem to be caught in a low-end equilibrium.
In order to act as a reliable third party, PES need to build a good and solid reputation among their main
clients by offering better and less time consuming pre-selection services. The use of machine-learning,
data-driven relevancy algorithms that calculate the viability of a specific candidate for a particular job
opening is becoming increasingly popular in this field. Based on the Portuguese PES databases (CVs,
vacancies, pre-selection and matching results), complemented by relevant external data published by
Statistics Portugal and the European Classification of Skills/Competences, Qualifications and
Occupations (ESCO), the current thesis evaluates the potential application of models such as Random
Forests, Gradient Boosting, Support Vector Machines, Neural Networks Ensembles and other tree-based
ensembles to the job matching activities that are carried out by the Portuguese PES, in order to
understand the extent to which the latter can be improved through the adoption of automated
processes. The obtained results seem promising and point to the possible use of robust algorithms such
as Random Forests within the pre-selection of suitable candidates, due to their advantages at various
levels, namely in terms of accuracy, capacity to handle large datasets with thousands of variables,
including badly unbalanced ones, as well as extensive missing values and many-valued categorical
variables
Fairness and Bias in Algorithmic Hiring
Employers are adopting algorithmic hiring technology throughout the
recruitment pipeline. Algorithmic fairness is especially applicable in this
domain due to its high stakes and structural inequalities. Unfortunately, most
work in this space provides partial treatment, often constrained by two
competing narratives, optimistically focused on replacing biased recruiter
decisions or pessimistically pointing to the automation of discrimination.
Whether, and more importantly what types of, algorithmic hiring can be less
biased and more beneficial to society than low-tech alternatives currently
remains unanswered, to the detriment of trustworthiness. This multidisciplinary
survey caters to practitioners and researchers with a balanced and integrated
coverage of systems, biases, measures, mitigation strategies, datasets, and
legal aspects of algorithmic hiring and fairness. Our work supports a
contextualized understanding and governance of this technology by highlighting
current opportunities and limitations, providing recommendations for future
work to ensure shared benefits for all stakeholders
AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions
It does not matter whether it is a job interview with Tech Giants, Wall
Street firms, or a small startup; all candidates want to demonstrate their best
selves or even present themselves better than they really are. Meanwhile,
recruiters want to know the candidates' authentic selves and detect soft skills
that prove an expert candidate would be a great fit in any company. Recruiters
worldwide usually struggle to find employees with the highest level of these
skills. Digital footprints can assist recruiters in this process by providing
candidates' unique set of online activities, while social media delivers one of
the largest digital footprints to track people. In this study, for the first
time, we show that a wide range of behavioral competencies consisting of 16
in-demand soft skills can be automatically predicted from Instagram profiles
based on the following lists and other quantitative features using machine
learning algorithms. We also provide predictions on Big Five personality
traits. Models were built based on a sample of 400 Iranian volunteer users who
answered an online questionnaire and provided their Instagram usernames which
allowed us to crawl the public profiles. We applied several machine learning
algorithms to the uniformed data. Deep learning models mostly outperformed by
demonstrating 70% and 69% average Accuracy in two-level and three-level
classifications respectively. Creating a large pool of people with the highest
level of soft skills, and making more accurate evaluations of job candidates is
possible with the application of AI on social media user-generated data
Evaluating student-internship fit by using fuzzy linguistic terms and a fuzzy OWA operator
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksPersonnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate’s interests, can be identified with fuzzy linguistic terms and a fuzzy OWA operator. A set of relevant positions aligned with a student’s interests is selected using this approach. The mplementation of the proposed method is illustrated using a numerical example in a business application.Postprint (author's final draft
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