Job Recommendation Based on Curriculum Vitae Using Text Mining

Apaza, Honorio; Vidal, Américo Ariel Rubin de Celis; Saire, Josimar Edinson Chire

doi:10.1007/978-3-030-73100-7_72

Cited by 5 publications

(3 citation statements)

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“…The Internet allows researchers and policy practitioners to access a large volume of information about jobs and job candidates. Most commonly, the data used originate from individual job sites (Beblavý, Kureková, and Haita 2016;Drahokoupil and Fabo 2022;Marinescu and Wolthoff 2020), as well as commercial websites where people post their personal profiles or CVs, such as LinkedIn or Indeed (Kureková and Žilinčíková 2018;Mamertino and Sinclair 2019;Apaza, Vidal, and Chire 2021;Pejic-Bach et al 2020). In some cases, however, data are instead taken from an aggregator, such as the European Public Employment Services network, EURES , or companies such as Emsi Burning Glass, which collect and extract information from large volumes of job vacancies from many websites (Hershbein 2016;Deming and Kahn 2018;Fabo and Kahanec 2020;Acemoglu et al 2020).…”

Section: Online Data In Labour Market Research: Trends and Characteri...mentioning

confidence: 99%

“…The large size of these markets generates a huge amount of labour market data: for instance, a recent study focusing on the Chinese market identified 20 million job adverts, offering 105 million job vacancies, posted on just one online platform in four months (Fang et al 2020). Other emerging and developing countries that have been studied include countries as diverse as the Philippines (Ecleo and Galido 2017), Ukraine (Muller and Safir 2019), Belarus (Vankevich and Kalinouskaya 2020), Kosovo (Brancatelli, Marguerie, and Brodmann 2020), Peru (Apaza, Vidal, andChire 2021), andMexico (Campos-Vazquez, Esquivel, andBadillo 2021).…”

Section: Online Data In Labour Market Research: Trends and Characteri...mentioning

confidence: 99%

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Methodological issues related to the use of online labour market data

Fabo¹,

Kureková²

2022

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This report provides a mapping of existing research that employs online labour market data, covering both online job vacancies (demand side) and online applicant data (CVs) (supply side). We discuss and assess a variety of tools and empirical methods that have been used to address specific disadvantages of this data, such as non-representativeness or fluctuations in data quantity and structure; these may be due to external shocks, such as the COVID-19 pandemic. We find that while this research field has expanded rapidly, including with respect to geographical coverage, many empirical studies do not engage with the methodological aspects and weaknesses of online labour market data and take them at face value. We highlight that there are legitimate research approaches, which are inductive in nature, focused on discovering patterns and trends in underlying data. These are by definition less concerned with generalizability of findings, as they have different objectives. For this body of research, online labour market data open new avenues for understanding developments in labour markets. We also argue that biases in online labour market data emerge due to multiple factors. With respect to the order of discrepancies between online labour market data and representative data sources, these are typically not paramount. Different techniques have been adopted to deal with the non-representativeness problem, such as statistical techniques; adapting the research questions and research focus to the quality of data; and use of mixed methods, including qualitative methods, to increase the robustness of results.

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Section: Online Data In Labour Market Research: Trends and Characteri...mentioning

confidence: 99%

Section: Online Data In Labour Market Research: Trends and Characteri...mentioning

confidence: 99%

Methodological issues related to the use of online labour market data

Fabo¹,

Kureková²

2022

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“…For optimizing the job preference prediction formula, historical delivery weight is determined by position descriptions. Similar user weight is computed from resume information [ 14 , 15 ]. A system of web scraping for automatic data collection from the web using markup HTML and XHTML (classical markup languages) has been presented in [ 1 ].…”

Section: Related Workmentioning

confidence: 99%

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.

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