Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry. (arXiv:2304.00006v1 [cs.IR])

The challenges of using inadequate online recruitment systems can be
addressed with machine learning and software engineering techniques.
Bi-directional personalization reinforcement learning-based architecture with
active learning can get recruiters to recommend qualified applicants and also
enable applicants to receive personalized job recommendations. This paper
focuses on how machine learning techniques can enhance the recruitment process
in the travel nursing industry by helping speed up data acquisition using a
multi-model data service and then providing personalized recommendations using
bi-directional reinforcement learning with active learning. This need was
especially evident when trying to respond to the overwhelming needs of
healthcare facilities during the COVID-19 pandemic. The need for traveling
nurses and other healthcare professionals was more evident during the lockdown
period. A data service was architected for job feed processing using an
orchestration of natural language processing (NLP) models that synthesize
job-related data into a database efficiently and accurately. The multi-model
data service provided the data necessary to develop a bi-directional
personalization system using reinforcement learning with active learning that
could recommend travel nurses and healthcare professionals to recruiters and
provide job recommendations to applicants using an internally developed smart
match score as a basis. The bi-directional personalization reinforcement
learning-based architecture with active learning combines two personalization
systems – one that runs forward to recommend qualified candidates for jobs and
another that runs backward and recommends jobs for applicants.