A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Martinez‐Gil, Jorge; Paoletti, Alejandra Lorena; Pichler, Mario

doi:10.1007/s10796-019-09929-7

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…Many top global companies have started adopting technology for TA (Phillips-Wren et al, 2016). Studies are discussing the technology for TAsocial media for recruitment (Phillips-Wren et al, 2016;Kapoor et al, 2018), AI for recruitment from candidate perspective (Van Esch et al, 2019), e-recruitment (Radhika and John, 2016;Melanthiou et al, 2015), automation of job offers (Martinez-Gil et al, 2016), blockchain technology for recruitment (Hassan Onik et al, 2018), the impact of technology on recruitment (Gupta and Baksi, 2016) and Internet use for recruitment (Kinder, 2000).…”

Section: Adoption Of Ai For Talent Acquisitionmentioning

confidence: 99%

Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations

Pillai¹,

Sivathanu

2020

BIJ

206

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PurposeHuman resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.Design/methodology/approachThis study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.FindingsThis research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.Practical implicationsThis paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.Originality/valueThis research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

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Section: Adoption Of Ai For Talent Acquisitionmentioning

confidence: 99%

Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations

Pillai¹,

Sivathanu

2020

BIJ

206

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“…During such expert validation, the quality of recommendations is inquired by a group of 'experts', which may be the researchers themselves, HR/recruitment experts, or sometimes students (e.g., [85]). Although the choice for expert validation is rarely discussed, we do find that for CBR and KB job recommender systems, approximately half of the contributions use expert validation [63,64,27,65,9,127,128,57,115,88,109,45].…”

Section: Validationmentioning

confidence: 97%

“…A common strategy to generate job recommendations is then to compute the similarity between the candidate profile and vacancy in the ontology space [104,64,88,65,127,128,48,109,6,83], where the overlap can be computed by for example the Jaccard index ( [48]). Although one can imagine that the construction of such ontologies can take considerable effort, they have been used successfully in practice by, for example, LinkedIn [75] or Textkernel [113].…”

Section: Knowledge-based Jrsmentioning

confidence: 99%

Job Recommender Systems: A Review

Ruijt¹,

Bhulai²

2021

Preprint

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This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as 'hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.

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“…There are many approaches to match the candidates profiles with the specific job offer, in the work (Rodriguez & Chavez, 2019) the author propose a system that adopt a clustering algorithm to match the profile of the job seekers and the requirements of the job posted by the employers. A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles is proposed in the work (Martinez-Gil et al, 2019), it is based on automatically computing transformation costs by using background knowledge (in the form of well-known taxonomies),this approach proposes a new method that involves collecting a wide range of partial measures, which can be strategically combined to replicate the behavior of the experts.…”

Section: Recommendation System In Human Resourcesmentioning

confidence: 99%

Recommendation System

Baali

2021

International Journal of Artificial Intelligence and Machine Learning

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The most challenging problem in human resources specially in the IT digital services company, is to assign the best collaborator’s in the adequate project , then ensure the delivery’s performance.in this paper we aim to develop à recommandation System using based-content and collaborative filtering in order to recommend potential profiles for a new job offer. The Principal parts of this recommandation is the matching between job offer of new project and collaborators profiles and the scoring using AHP method. In the first step we propose a model of criteria to measure collective skills , we validate by a survey realized in the IT service company , we analyze the data collected using PCA method (Principal Component Analysis).the results indicate six factors to measure collective skills of each collaborator (Technical skill, Proactivity ,Integrity, Cooperation, Communication and Benevolence/Interpersonal Relationship), these factors are used in AHP function to give score for each collaborator then allow the recommendation for the adequate project.

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A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Cited by 16 publications

References 27 publications

Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations

Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations

Job Recommender Systems: A Review

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