• Resumo

    AutoRGNN: um Modelo de Previsão de Churn que preserva a Privacidade com uma Abordagem Híbrida de Aprendizado Profundo

    Data de publicação: 03/05/2023

    In recent years, the use of mobile applications for digital service
    is being widely deployed in a varied range of contexts. With this,
    predicting the possibility of churn is vital for selecting users that
    could be targeted with user-retention campaigns. This technique
    is commonly referred as Churn Prediction Problem (CPP). Most
    studies in the literature use traditional machine learning techniques
    to predict churn, and neglect the users’ privacy. In this work, we
    propose a privacy-preserving solution that uses neural network to
    predict churn of mobile services. Our solution, called AutoRGNN,
    requires only the installation and uninstallation sequences of mobile
    apps, and integrates Recurrent and Graph Neural Networks. In
    comparison with a traditional baseline approach in a large-scale
    and real scenario, AutoRGNN was capable to increase the recall
    and precision up to 19% and 7%, respectively.

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