Applying Learning-to-Rank to Human Resourcing's Job-Candidate Matching Problem: A Case Study.
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2017-08-17
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en
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Abstract
A 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).
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Faculteit der Sociale Wetenschappen