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The challenges of using inadequate online recruitment systems can beaddressed with machine learning and software engineering techniques.Bi-directional personalization reinforcement learning-based architecture withactive learning can get recruiters to recommend qualified applicants and alsoenable applicants to receive personalized job recommendations. This paperfocuses on how machine learning techniques can enhance the recruitment processin the travel nursing industry by helping speed up data acquisition using amulti-model data service and then providing personalized recommendations usingbi-directional reinforcement learning with active learning. This need wasespecially evident when trying to respond to the overwhelming needs ofhealthcare facilities during the COVID-19 pandemic. The need for travelingnurses and other healthcare professionals was more evident during the lockdownperiod. A data service was architected for job feed processing using anorchestration of natural language processing (NLP) models that synthesizejob-related data into a database efficiently and accurately. The multi-modeldata service provided the data necessary to develop a bi-directionalpersonalization system using reinforcement learning with active learning thatcould recommend travel nurses and healthcare professionals to recruiters andprovide job recommendations to applicants using an internally developed smartmatch score as a basis. The bi-directional personalization reinforcementlearning-based architecture with active learning combines two personalizationsystems - one that runs forward to recommend qualified candidates for jobs andanother that runs backward and recommends jobs for applicants.

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