Recruitment Market Trend Analysis with Sequential Latent Variable Models

Zhu, Chen; Zhu, Hengshu; Xiong, Hui; Ding, Pengliang; Xie, Fang

doi:10.1145/2939672.2939689

Cited by 51 publications

(17 citation statements)

References 27 publications

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“…Job skills of talents are demonstrated to be valuable to measure their popularity on the job markets [7,30]. Recently, researchers are devoted to analyze recruitment market from more novel hiring perspectives [12], using market trend analysis [35] and job interview assessment [25].…”

Section: Related Workmentioning

confidence: 99%

Interview Choice Reveals Your Preference on the Market

Yan

Song³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Online recruitment services are now rapidly changing the landscape of hiring traditions on the job market. There are millions of registered users with resumes, and tens of thousands of job postings available on the Web. Learning good job-resume matching for recruitment services is important. Existing studies on job-resume matching generally focus on learning good representations of job descriptions and resume texts with comprehensive matching structures. We assume that it would bring benefits to learn the preference of both recruiters and job-seekers from previous interview histories and expect such preference is helpful to improve job-resume matching. To this end, in this paper, we propose a novel matching network with preference modeled. The key idea is to explore the latent preference given the history of all interviewed candidates for a job posting and the history of all job applications for a particular talent. To be more specific, we propose a profiling memory module to learn the latent preference representation by interacting with both the job and resume sides. We then incorporate the preference into the matching framework as an end-to-end learnable neural network. Based on the real-world data from an online recruitment platform namely "Boss Zhipin", the experimental results show that the proposed model could improve the jobresume matching performance against a series of state-ofthe-art methods. In this way, we demonstrate that recruiters and talents indeed have preference and such preference can improve job-resume matching on the job market. * indicates equal contribution.

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Section: Related Workmentioning

confidence: 99%

Interview Choice Reveals Your Preference on the Market

Yan

Song³

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…Recruitment is always a core function of human resource management to support the success of organizations. Recently, the newly available recruitment big data enables researchers to conduct recruitment analysis through more quantitative ways [12,16,22,35,36,41]. In particular, the study of measuring the matching degree between the talent qualification and the job requirements, namely Person-Job Fit [29], has become one of the most striking topics.…”

Section: Recruitment Analysismentioning

confidence: 99%

“…Indeed, as a crucial task for job recruitment, Person-Job Fit has been well studied from different perspectives, such as job-oriented skill measuring [36], candidate matching [23] and job recommendations [21,28,40]. Along this line, some related tasks, such as talent sourcing [35,41] and job transition [33] have also been studied. However, these efforts largely depend on the manual inspection of features or key phrases from domain experts, and thus lead to high cost and the inefficient, inaccurate, and subjective judgments.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Person-Job Fit for Talent Recruitment

Qin

Zhu

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

Self Cite

107

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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 (APJFNN) 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 (RNN). 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 realworld data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines.

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“…Anastasiu et al , 2017). The analysis of the skills demand in the labour market has received a considerable boost since the recent work by Zhu et al (2016) that analyzed job skills popularity from online job posting platforms. Similarly, Boselli (2018) and colleagues adopted job vacancy titles and descriptive texts to infer the popularity of job skills required in the labour market over time.…”

Section: Methodology Of Researchmentioning

confidence: 99%

Who is interested in retail education? The (mis)match between the leading universities’ offerings and job demand in the UK

Pantano

Pizzi

Rogers

2020

IJRDM

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PurposeRetail management has acquired the attention of scholars and practitioners, with many international and prestigious journals specifically relating to the topic. Also, top-tier journals in other disciplines have proposed special issues on the new advances in retailing, with emphasis on the role of new and smart technologies. On the one hand, the research in retailing seems to be prolific; on the other hand, the interest in retail education (from a research and university perspective) seems to be more limited. The purpose of this paper is to capture the (mis)match between the leading universities' offerings and job demand in the UK. In this way, the paper identifies opportunities for educators and researchers to educate future career-ready professionals in retailing and improve research in retail education.Design/methodology/approachThe research evaluates the offer of UK retail education in terms of programmes/courses, focusing on the Russell Group universities for the academic year 2020/2021 (September starts) and the demand of certain skills and competences by the largest retailers in the UK. The study utilizes secondary data based on the courses/programmes specifically related to the retail sector and on the job opportunities through the leading UK grocery retailers.FindingsThe findings reveal the extent of the gap between the university educational offerings and the requirements from retailers.Originality/valueTo the best of authors’ knowledge, this paper is the first attempt to capture and compare multiple evidence bases related to academic curriculums and employers' requirements for specific retail competencies.

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Recruitment Market Trend Analysis with Sequential Latent Variable Models

Cited by 51 publications

References 27 publications

Interview Choice Reveals Your Preference on the Market

Interview Choice Reveals Your Preference on the Market

Enhancing Person-Job Fit for Talent Recruitment

Who is interested in retail education? The (mis)match between the leading universities’ offerings and job demand in the UK

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