Applying Learning-to-Rank to Human Resourcing's Job-Candidate Matching Problem: A Case Study.

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dc.contributor.advisorFarquhar, J.D.R.
dc.contributor.advisorRodenburg, K.
dc.contributor.advisorKasperink, H.
dc.contributor.authorBraun, H.
dc.contributor.otherNCIMen_US
dc.date.issued2017-08-17
dc.description.abstractA challenge that every company or organization will continue to face regularly is the task of recruiting people that will perfectly fit their vacant jobs. This is especially vital for companies in the Human Resourcing industry, like employment agencies and secondment companies, whose livelihood depend on selecting the right candidates. This selection process is currently performed by Human Resourcing professionals. The first step commonly consists of manually searching through the available applicants, eventually producing a list of suitable candidates that get invited to the next phase of the application process. This already labor intensive process has been made even more challenging with the advent of online job recruitment. This made finding and posting vacancies simpler, but also increased the number of applicants. However, the digitization of recruitment can alleviate this information over- load by, for example, providing the Human Resourcer with an ordering of the applicants, based on their estimated suitability for a given position. For the NCIM-Group, a secondment company near Leidschendam, Learning- to-Rank, a type of Machine Learning, was applied to automatically induce a way to do just this: to order a list of candidates based on a given job offer. The ranking model was learned from the company's historical placement data. There are many ways of solving the learning-to-rank problem. Three state- of-the-art models, each one exemplifying one of the three common approaches, the point- pair- and list-wise approach, have been implemented to identify which one is best suited for this problem. Speci fically, a Gradient Boosted Regression Trees (GBRT) model (a point-wise method), a LambdaMART model (a pair- wise method) and a SmoothRank model (a list-wise method) were applied. Their performance, plus a baseline Best-Single-Feature model, were compared with the existing Evolutionary Algorithm model on two common rank-based evaluation measures: Mean Average Precision (MAP) and mean Normalized Discounted Cumulative Gain (NDCG). All three methods improved the performance signi ficantly compared to the existing algorithm, with an increase in MAP score up to 59.7% (GBRT-model vs. Evo.-model, p = 0:0001). Additional results indicate that adding Manifold Regularization, a semi- supervised technique, to SmoothRank may improve its performance slightly by 6% (although not statistically significant).en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/5260
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/5260
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleApplying Learning-to-Rank to Human Resourcing's Job-Candidate Matching Problem: A Case Study.en_US
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