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

Description:

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...

Keyword:

Reinforcement learning, Active learning, Contextual Bandit, NLP, NER, Sentiment Analysis, Counter factual Evaluation, Travel Industry, Unit tests, Serverless Functions, Language Understanding, Azure Cosmos DB, Serverless Functions, Azure Personalizer,

Subject:

Natural language processing (Computer science), Artificial intelligence, Sentiment analysis, Reinforcement learning, and Machine learning

Creator:

Ezana N. Beyenne

Owner:

Ezana Negga Beyenne

Publisher:

Data Science Masters Theses

Date Uploaded:

03/15/2023

Date Modified:

03/15/2023

Resource Type:

Dissertation and Masters Thesis