Public services recommendation system: an alternative to customize the digital government transformation

Abstract

The market success in the application of recommendation systems technologies consolidates them as a mechanism of strong relationship with the consumer. However, it is still little explored in digital government scenarios, mainly in strengthening the relationship between public administration and the citizen. This study focuses on the application of recommendation systems in digital government services, in the context of Brazilian state of Mato Grosso, with the implementation of machine learning algorithms, based on citizens' access to public services, personalizing their journey and recommending other services and information due to the similarity between the data. In an exploratory way, bibliographic surveys were carried out with content analysis. The results include a platform with a process and architecture for implementing the new model. It is also presented an important discussion about diversity and novelty and the consequent improvement in the citizen's experience, preventing the monotony and predictability of digital government systems.