Demographic prediction of mobile users based on machine learning

Chuhan Chen

doi:10.1117/12.2673540

14 April 2023 Demographic prediction of mobile users based on machine learning

Chuhan Chen

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130O (2023) https://doi.org/10.1117/12.2673540
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

In the growing market of mobile applications, users generate large amounts of mobile log data every day, which can be used for mobile users’ demographic prediction. However, the complexity of raw log data increases the difficulty of predicting demographics, and noises in these data also negatively affect the predictions in terms of accuracy. To address these issues, we construct features based on the raw log data to obtain mobile users’ behavioral data. We also propose a two-stage model using Logistic Regression (LR), Random Forest (RF), XGBoost, and ensemble methods (Averaging, Majoring, and Weighted) to predict users’ gender groups in Stage I and then age groups in Stage II. Results show that RF achieves the highest prediction accuracy in both gender prediction and age prediction, and Averaging Ensemble achieves the highest Area Under the Curve (AUC) score. Moreover, our research is significant because the two-stage model is interpretable and applicable.

Citation Download Citation

Chuhan Chen "Demographic prediction of mobile users based on machine learning", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130O (14 April 2023); https://doi.org/10.1117/12.2673540

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