TY - JOUR
T1 - Modeling Spatio-Temporal App Usage for a Large User Population
AU - Wang, Huandong
AU - Li, Yong
AU - Zeng, Sihan
AU - Wang, Gang
AU - Zhang, Pengyu
AU - Hui, Pan
AU - Jin, Depeng
PY - 2019/3/29
Y1 - 2019/3/29
N2 - With the wide adoption of mobile devices, it becomes increasingly important to understand how users use mobile apps. Knowing when and where certain apps are used is instrumental for app developers to improve app usability and for Internet service providers (ISPs) to optimize their network services. However, modeling spatio-temporal patterns of app usage has been a challenging problem due to the complicated usage behavior and the very limited personal data. In this paper, we propose a Bayesian mixture model to capture when, where and what apps are used and predict future app usage. To solve the challenge of data sparsity, we apply a hierarchical Dirichlet process to leverage the shared spatio-temporal patterns to accurately model users with insufficient data. We then evaluate our model using a large dataset of app usage traces involving 1.7 million users over 3503 apps. Our analysis shows a clear correlation between the user's location and the apps being used. Extensive evaluations show that our model can accurately predict users' future locations and app usage, outperforming the state-of-the-art algorithms by 11.7% and 11.1%, respectively. In addition, our model can be used to synthesize app usage traces that do not leak user privacy while preserving the key data statistical properties.
AB - With the wide adoption of mobile devices, it becomes increasingly important to understand how users use mobile apps. Knowing when and where certain apps are used is instrumental for app developers to improve app usability and for Internet service providers (ISPs) to optimize their network services. However, modeling spatio-temporal patterns of app usage has been a challenging problem due to the complicated usage behavior and the very limited personal data. In this paper, we propose a Bayesian mixture model to capture when, where and what apps are used and predict future app usage. To solve the challenge of data sparsity, we apply a hierarchical Dirichlet process to leverage the shared spatio-temporal patterns to accurately model users with insufficient data. We then evaluate our model using a large dataset of app usage traces involving 1.7 million users over 3503 apps. Our analysis shows a clear correlation between the user's location and the apps being used. Extensive evaluations show that our model can accurately predict users' future locations and app usage, outperforming the state-of-the-art algorithms by 11.7% and 11.1%, respectively. In addition, our model can be used to synthesize app usage traces that do not leak user privacy while preserving the key data statistical properties.
U2 - 10.1145/3314414
DO - 10.1145/3314414
M3 - Conference article
SN - 2474-9567
VL - 3
SP - 1
EP - 23
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
IS - 1
ER -