A Few-Shot Learning Framework for Understanding Mobile User Behavior
DOI:
https://doi.org/10.22581/muet1982.0155Keywords:
Few-Shot Learning, Meta-learning, Mobile data features, App usage predictionAbstract
It is vital to understand the behavior of mobile users to provide them with personalized experiences, but obtaining large amounts of labelled data is difficult and expensive. In this paper, we explore using few-shot learning to overcome this challenge by allowing models to learn from only a few labelled examples. We focus on the classification of mobile users on behavior data, such as app usage, screen time, battery drain, and data usage. Using a meta-learning approach, our model is trained over 50 epochs with continuous monitoring of loss and accuracy. The results are promising; it shows a test accuracy of 98.75% and a loss reduction that shows that few shot learning can be an effective solution even when data is limited. This approach opens up new possibilities for analyzing mobile user behavior without requiring large amounts of labelled data, making it more practical for real-world applications.
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References
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