A recommender system for occupational hygiene services using natural language processing
Although recommender systems have been used in a wide variety of products, they have not had the same adoption in the insurance sector due to the particular aspects of the industry. The present research proposes a novel model that uses natural language processing techniques and a deep learning-based model to recommend occupational hygiene services. The results of ranking methods showed that the model combining memorization and generalization of companies’ activities preferences performed better compared to other models. These findings have significant implications for decision-making as they can improve the welfare of both companies and workers, while also limiting the options offered to clients based on their unique profiles.
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