Bi-directional personalization reinforcement learning-based architecture with  active learning using a multi-model data service for  the travel nursing industry

Ezana N. Beyenne

doi:https://doi.org/10.21985/n2-f3be-my39

Work

Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry

Public Deposited

Download PDF

Download All Files (.zip)

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. The reinforcement learning based personalization system uses a custom smart match scoring system to develop a score ranging from 0 (not recommended) to 1 (a perfect match for the job) and then display the top twenty matches to the end user that could be recruiters looking for candidates for each job, or jobs for applicants. The end user then actively gives feedback which the system uses to improve personalized recommendations. The system also uses a batch training process to improve the overall recommendations while also considering other factors, such as fairness

Last modified