Online Job Portals Research Articles

From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.

Online Job Portal

ABSTRACT___ The Online Job Portal is a web application developed using the Python Django framework. This project aims to provide a platform for job seekers and companies to interact and fulfill their employment needs. The application allows job seekers to create profiles, search and apply for jobs, and receive updates on their application status. Companies can post job vacancies, search for suitable candidates, and manage the hiring process. The evolution of technology has revolutionized the job search and recruitment process, giving rise to online job portals as essential tools for job seekers and employers. This abstract presents the concept and development of a comprehensive online job portal designed to bridge the gap between job seekers and employers efficiently. INDEX TEARMS – HTML, CSS , JavaScript, Python, Django Framework, SQLlite.

Evaluation of online job portals for HR recruitment selection using AHP in two wheeler automotive industry: a case study

Evaluation of online job portals for HR recruitment selection using AHP in two wheeler automotive industry: a case study

Shocking Offers: Gender, Wage Inequality, and Recessions in Online Labor Markets

Using data from the largest online job portal in Nigeria, we document (i) gender differences in salary offers for jobs and (ii) the response of (i) to recessions. Jobs in industries where the number of job applicants skews female offer lower starting salaries than jobs in industries where applicants skews male. During Nigeria's 2016 recession, applications to jobs in industries that skew female fell, and salary offers rose. In contrast, applications to jobs in industries that skew male rose, and salary offers fell. In accordance with this relative shift in applications, in 2016 the salary-offer gap almost disappears.

A Comprehensive Review of the Three Main Topic Modeling Algorithms and Challenges in Albanian Employability Skills

Today’s jobseekers face many obstacles while trying to find a career that aligns with their interests, employability soft skills, and professional experience. In Albania, jobseekers frequently initiate their job search by actively exploring job vacancies listed on various online job portals. The analysis of job vacancies posted online provides an added advantage to the labour market actors compared to traditional survey-based analyses. This is because it enables a faster analytical process, promotes decision-making based on accurate data, and should be carefully considered by every country when formulating their Labor Market Policies. Since the data posted online are unlabelled, it has been proven that the potential of unsupervised learning techniques, more precisely the Topic Modelling algorithms, is outstanding when applied to analysing job vacancies, mainly with regard to assessing employability soft skills. Algorithms in topic modelling are essential for uncovering hidden patterns in texts, facilitating the extraction of important data, generating document summaries, and enhancing content comprehension. This paper analyses and compares the three primary methodologies and algorithms used in topic modelling, which can be applied to analyse employability soft-skills: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and BERTopic. At the end of the paper, conclusions are drawn regarding superior performance and optimal algorithm applicability, challenges, and limitations through a review of studies conducted in the Albanian job market.

Використання великих даних для інформаційного забезпечення ринку праці

The challenges of collecting current labor market data amidst global changes and geopolitical fluctuations increasingly necessitate new approaches and alternative methods of data collection and analysis. This highlights the relevance of research aimed at developing and adapting labor market information provision methods to contemporary challenges. A promising approach in this context is the use of Big Data for labor market assessment, which involves collecting information from sources such as online job search and vacancy portals. This method allows for a deeper analysis of market trends and provides a more accurate and timely assessment of labor market needs and opportunities. The aim of this article is to discuss approaches to developing labor market information systems using Big Data, particularly online data from job vacancy websites. It examines the use of Big Data in labor market analysis based on a database containing over four million job vacancies posted on Ukrainian job search portals over the last five years, provided by the European Training Foundation (ETF). The effectiveness of these approaches in facilitating job search for all interested parties is evaluated, particularly through providing insights into the dynamics of supply and demand in the labor market based on data from these portals. The opportunities and limitations of using Big Data in this context are analyzed, including their impact on employment policy development and labor market planning. The potential benefits of Big Data in providing deeper and more accurate market condition analyses are outlined, along with technical aspects and challenges associated with their processing and interpretation. The article examines methodological approaches to data collection and analytical processing in the context of accelerated transformations, volatility, and limited access to traditional information resources. The scientific novelty of the article lies in the substantiation of the feasibility and appropriateness of using open data from online job portals for labor market information provision under current conditions. In conducting the research, methods of analysis, synthesis, and generalization were applied to identify the main contemporary issues of labor market information provision in Ukraine. The effectiveness of data collection methods based on web scraping and parsing techniques was evaluated, as well as the use of the integrated Snowflake platform to identify key trends and patterns in the labor market. The conclusions summarize the main points and substantiate directions for further research, highlighting the significance of Big Data in developing employment strategies and optimizing the labor market.

Perception of Job Seekers on Recruitment Through Online Job Portals

Recruitment is a positive process where an organization searches for prospective employees and stimulate them to apply for the job. On the other hand, the job seekers too are in search of organisations offering them employment. So therefore it is a mutual process where recruitment helps in binding both job seekers and organisation together. Recruitment through social media or through online job portals is a vast subject which has become an integral part of recruitment process. It helps the job seekers in reaching out to the organisation, helps them in keeping themselves aware of new development and connect with the world around them. It is because of the above reason that online recruitment has crafted innovativeness in HR practices and processes. Traditional recruitment processes are vanishing slowly and online job portals are growing rapidly and are in the stages of being formulated and adopted which for many human resource practioners it is a new tool for the purpose of recruitment. In this growing world it is very important for the companies to make a right hiring decision and for the job seekers to know their preference towards such practices. This research paper aims to explore the perceived advantages on online recruitment to find out the preference of job seekers as far as different types of web based recruitment is concerned and to know the influences of various demographic factors effecting on job seekers interaction with online job portals.

Bidirectional recommendation in HR analytics through text summarization

For over a decade, online job portals have been providing their services to both job seekers and employers in search of hiring opportunities. Because of the high demand for recruitment, it is insufficient to use conventional hiring methods to find a suitable candidate to fill the position. Validating resumes online is challenging due to the potential for manual errors, making the process inherently risky. The bidirectional method comprises named entity recognition (NER) for extracting the required resumes for recruiters. Cosine similarity shows the match percentage of resumes for the job requirements and vice versa. In an attempt to tackle an issue of unregistered words, a solution called decoder attention with pointer network (DA-PN) has been introduced. This method incorporates the use of coverage mechanism to prevent word repetition through generated text summary. DA-PN+Cover method with mixed learning objective (MLO) (DA-PN+Cover+MLO) is utilized for protecting grow of increasing faults in generated text summary. Performance of proposed method is estimated using evaluation indicator recall oriented understudy for gisting evaluation (ROUGE) and attains an average of 27.47 which is comparatively higher than existing methods.

Enhanced Empowering Futures through Online Job Portal

This research aims to develop a job web portal for the students in the Faculty of Computer Science and Information Technology. The main aims of this portal are to connect with industries and act as an online recruitment tool to support students in finding the right IT job after graduation. Furthermore, this system enhances the understanding of the concept and importance of the job portal for students in universities. A survey was conducted to identify the students’ problems with the existing portal of the faculty and to gather their requirements, which can be incorporated into the portal to be developed.

Hiring Frictions and the Promise of Online Job Portals: Evidence from India

Despite the growing prominence of online job portals, firms remain reluctant to hire outside traditional recruitment networks. We find that experimentally providing firms with a combination of advertising and the ability to verify applicant identity increases portal-based hiring by 68 percent and the likelihood of filling a vacancy by 11 percent. Advertising attracts more skilled applicants, while verification enables the screening of unfamiliar applicants. Portal-based hires are retained beyond the standard assessment period, suggesting that they are well suited to the vacancies. Firms assigned only advertising also attract more skilled applicants, but providing neither advertising nor verification alone increases hiring. (JEL D22, J23, J24, J63, M51, O15)

ATTITUDES OF ECONOMICS STUDENTS TO SEARCHING FOR JOBS THROUGH SOCIAL MEDIA: THE CASE OF KOSOVO

Abstract. The article examines how the landscape of social networks has significantly transformed the job search process. The objective of the study was to evaluate attitudes to searching for a job on social media with a focus on students attending the Faculty of Economics, University of Prishtina. The findings indicate that selfperceived level of social media activity is associated with time spent on social networks, and that unemployed individuals are more likely than those with jobs to use social media. The results show the most popular methods used for job searching were social media networks and online job portals. A noteworthy finding is that even individuals who do not use social networks recognise the importance of maintaining a professional profile on social media when it comes to searching for work. The study’s findings hold implications for both job seekers and employers by highlighting the importance of using social media while searching for a job, necessitating strategic investments in online platforms, customised support programmes harnessing these platforms, enhanced digital literacy, and continuous research to adapt policies in response to evolving social media trends among job seekers. Keywords: social media, job searching, recruitment, digitalisation

Job Recommendation System Using Deep Reinforcement Learning (DRL)

The rapid growth of online job portals and the increasing volume of job listings have made it challenging for job seekers to efficiently navigate through the vast number of available opportunities. Job recommendation systems play a crucial role in assisting users in finding relevant job opportunities based on their skills, preferences, and past experiences. This research paper proposes a job recommendation system that leverages deep learning techniques to enhance the accuracy and effectiveness of job recommendations. The system utilizes advanced algorithms to analyses user profiles, job descriptions, and historical data to generate personalized job recommendations. Experimental evaluations demonstrate the superiority of the proposed system compared to traditional recommendation methods, thereby improving the job search process for both job seekers and employers. This paper provides Job recommendation system using Deep Reinforcement Learning (DRL).

Expectations of employers in the United States for entry-level public health job skills with a bachelor's degree: an analysis of the positions advertised in an online job portal.

To analyze the current public health labor market for bachelor's-level graduates. Public health-related job postings (N = 365) from across the United States were collected from an online job portal, Indeed.com, from November 7-14, 2022. Job titles, organization type, degree requirements, work experience, job location, and preferred skills for bachelor's-level public health positions were analyzed. Thirty-one job titles were identified. Approximately one-third (32.33%) of postings were from the Northeastern US; 23.56% were from the Southeastern region. Thirty-five job skill categories were identified. Most jobs (92.33%) required oral and written communication skills, and 85.21% specified educational skills for public health promotion. Cultural competency, project management, and case management abilities were also highly sought. This study revealed the needs of the public health workforce and bolstered the case that public health degree-seekers should be equipped with a set of strategic skills applicable to a range of multisectoral and multidisciplinary public health jobs. Given the rapid changes in the field of public health, ongoing analysis of the labor market benefits educators, employers, and policymakers alike.

To see, or not to see: Online job advertisement and EU non-discrimination law

The recruitment process has largely moved online. Job advertisements which used to be bound to newspapers and other print media have become an online service as part of a growing trend towards a more digitalised hiring process. Alongside increased flexibility and cost-cutting, this trend brings so previously unseen challenges. The technology behind online job portals and social media allows job ads to be shown to targeted groups of people using machine learning techniques to filter through the available data and search for the most suitable audience. The correlations that are inferred by algorithms between content and audience, however, can lead to biased outcomes. This is a serious problem since the specific risk with online job ads is that jobseekers who are considered less suitable by the algorithm will not see the ad at all. Such a result effectively hinders access to the labour market and poses the risk of perpetuating existing biases and discrimination. Those discrimination risks raise questions about the legal framework of online job advertisements. This article examines the requirements of the new EU initiatives to regulate artificial intelligence and the digital market and EU non-discrimination law regarding online job advertisements. It also proposes a low-tech solution to the high-tech problems associated with online job advertisements by introducing a legal requirement to publicly tender job ads on an online noticeboard, thus ensuring transparency and effective access to employment.

How attractive are internship advertisements to prospective applicants? A mixed-method examination through the lens of employer branding

PurposeThe purpose of this study was to investigate attractiveness of internship advertisements to the prospective applicants through the lens of employer branding. By giving attention to internship attractiveness, universities and companies expand current collaborations around internship provision to enhance student internship experiences, satisfaction and employment prospects and to foster long-term sustainability of internship programs.Design/methodology/approachThe study used a mixed-methods design employing a concurrent triangulation strategy. The authors conducted a content analysis of 94 internship listings published in the largest Russian job portal, HeadHunter, and collected surveys from 274 prospective interns.FindingsThe study shows a mismatch between employer branding strategies and prospective interns' perceptions of employer attractiveness. Companies emphasize economic value and visual identity as well as functional attributes, while prospective interns prioritize development value and symbolic attributes. The findings suggest internship advertisements do not appeal to potential applicants.Research limitations/implicationsThe results are limited to business students from specific universities in Russia, so further research is needed to include students with different majors and from other countries. The study is also limited to advertisements available via a large online job portal. A comparative analysis of effectiveness of various communication channels for internship promotion could yield useful results.Practical implicationsCareer center advisors, academic program directors and internship coordinators should work with company leaders, human resources and marketing decision-makers, and recruitment specialists to better understand interns' drivers of attraction and audit the internship advertisements. Employers could use the study results to strengthen recruiting messages and, hence, attractiveness of the employer brand to potential interns.Originality/valueThe study contributes by applying the employer branding theory to an understudied population of interns, hence providing new insights into internships as collaboration between universities and companies. By focusing on internship positions, which differ from full-time permanent positions across many aspects, including the positions' duration, pay, supervision and purpose, the study assists in understanding the distinct drivers of attraction of interns, which are missing from published research on the topic.

ONLINE JOB PORTAL USING DJANGO

Our current generation uses the internet for everything right from shopping to getting hired. In this project, we attempt to address and minimize the gap between the job seeker and the recruiter through this ONLINE JOB PORTAL (Web Application) using python Django. This is done by considering details of both the job seeker and the recruiter and by applying a variety of filters to satisfy their requirements. Job seekers can search for vacancies according to their qualifications, skills, and experience as well as a recruiter can easily find suitable and eligible candidates using the job seeker’s resume and profile details. In addition to this, there will be an administrator to manage and authenticate the system services. We are using MVT, a software designing architecture, to design this system. In the existing system, they have the book store feature, where users can access books based on their requirements but in our project, we are providing informative videos on how to face interviews and videos regarding their career necessities; the existing system provides updates on new job availabilities posted (within 2–3 days) but we are going to provide updates on all job availabilities posted within the past 15 days along with their opening and closing dates and also add the feature of detecting the search errors using LIKE operator (pattern matching) and string matching algorithm.

But which skills?

Skills assessment is essential for today’s labour market. There are many factors that change the requirements for the workplace. More than ever, it is important to monitor which skills are in high demand so that workers stay employed and companies do not lose productivity. This research discusses the relevance of data from online job portals for this task. It then uses a skill extractor in online job advertisements from Chile to identify and extract the skills employers place in their online job advertisements through skills dictionaries. The study shows modest results when using the European Skills, Competences and Occupations (ESCO) dictionary but an enhanced and much-improved result when adding an inductively constructed dictionary of the national labour market. Using this method would allow a new input of information to be incorporated into labour market information systems that would enable better decisions to be made by the various actors in the labour market.

Online Resume Builder Using Django

A resume is a record utilized by people to offer their historical past and ability sets. A record with a quick precis or listing approximately applicable training and experience. The resume or CV is normally the primary object that a capacity person encounters concerning the process seeker and is by and large used for screening applicants that are frequently observed via way of means of an interview, at the same time as in search of employment with inside the process seeks system and well-designed resume. The online job portal using Django will assist a person to construct his or her non-public commercial via the resume builder gadget to expand a resume with a process placement gadget. Many big employers use digital resume processing structures to deal with a big wide variety of resumes. Job portal commercials might also additionally direct candidates to email their resume to their business enterprise or go to their internet site and post a resume in digital format. Online jobs sought via famous websites are useful as they ve served for many years as a distinguished seek device for process seekers and employers alike. Despite their treasured application in linking employers with the capacity employees, the looking system and generation utilized by process-looking websites have now no longer saved tempo with the fast modifications in computing functionality and gadget intelligence. The Information and records retrieval strategies utilized by those websites mainly relies upon manually entered seek queries with a few superior similarity metrics for rating seek result.

Online Job Portal- A Better Way for Finding Jobs in the COVID-19 Pandemic

Abstract: Due to the COVID-19 pandemic unemployment broke all the previous records. Due to the pandemic students cannot go to colleges and have no option of campus placements. The only option available for them is to find a job through online job portals. In this paper we present the online recruitment framework that enables different companies to post their job vacancies, which jobseekers can consider while searching for jobs. This portal can also capture job requirements dependent on employers’ needs. A survey was conducted to identify the problems and requirements of the users with the current job portals and the findings of the survey are incorporated in this portal. The main aim of this job portal is to connect the employers and jobseekers as an e-recruitment to help jobseekers find the right jobs. Keywords: Job Seeker, Employer, Online Job Portal, Online Recruitment, Knowledge Sharing, COVID-19 Pandemic

Does Employer-Paid, Job-Protected Maternity Leave Help or Hurt Female IT Workers? Evidence from Millions of Job Applications

Paid maternity leave is considered an important pathway to increasing women’s participation in IT because it helps address work and family balance concerns, which are pronounced in the field due to long hours, travel, and the need to keep up with technology. Many governments require that employers pay for maternity leave. Using micro-level data on millions of applications from an online job portal, our study examines whether a policy change in India that expanded employer-paid, job-protected maternity leave from short-term to medium-term (12 to 26 weeks) changes companies’ likelihood of considering female applicants to IT jobs and women’s likelihood of applying to such jobs. We find that post-legislation, there is a 4% decrease in the probability of women being invited for interviews and an 8% increase in the probability of a female applicant. Interestingly, in regions with low women’s labor force participation rates, we find a 31% decrease in the probability of women being invited for an interview, post legislation. In contrast, regions with high rates did not change their hiring practices, suggesting that the reform may increase regional inequality in women’s representation. Post legislation, women were significantly more likely to apply to jobs in regions with high women’s labor force participation rates relative to those with low rates. Our study identifies an important source of leakage in the pipeline for female IT workers and may inform policies aimed at reducing gender gaps in representation.