AppUsage2Vec: Modeling Smartphone App Usage for Prediction

2019 IEEE 35th International Conference on Data Engineering (ICDE)(2019)

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App usage prediction, i.e. which apps will be used next, is very useful for smartphone system optimization, such as operating system resource management, battery energy consumption optimization, and user experience improvement as well. However, it is still challenging to achieve usage prediction of high accuracy. In this paper, we propose a novel framework for app usage prediction, called AppUsage2Vec, inspired by Doc2Vec. It models app usage records by considering the contribution of different apps, user personalized characteristics, and temporal context. We measure the contribution of each app to the target app by introducing an app-attention mechanism. The user personalized characteristics in app usage are learned by a module of dual-DNN. Furthermore, we encode the top-k supervised information in loss function for training the model to predict the app most likely to be used next. The AppUsage2Vec was evaluated on a dataset of 10,360 users and 46,434,380 records in three months. The results demonstrate the state-of-the-art performance.
Predictive models,Hidden Markov models,Markov processes,Bayes methods,Batteries,Computational modeling,Data models
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