App Usage Prediction

Predicting mobile app usage aims to anticipate user behavior by analyzing diverse data sources, including app usage history, location data, and contextual information, to improve personalized services and resource management. Current research focuses on developing sophisticated models, often leveraging transformer-like architectures and large language models, to handle the complexity of sequential data and address challenges like the "cold start" problem for new users. These advancements are improving the accuracy and efficiency of app usage prediction, with implications for personalized app recommendations, targeted advertising, and even socio-economic analysis based on aggregated, privacy-preserving data.