App usage prediction for dual display device via two-phase sequence modeling

Abstract There have been many recent studies dealing with issues regarding app usage prediction. A number of smartphone manufacturers have recently released flexible dual display devices for multitasking purposes. In spite of many studies achieving higher levels of prediction accuracy, owing to certain practical limitations, they have not been effectively applied to real dual display devices. First, they trained their prediction model using only existing data collected from a single display device. Thus, they are unable to reflect the multitasking usage of dual display devices in their model prediction. In addition, previous studies have mostly been based on a Markov assumption, which needs to calculate the transition probability for all pairs of apps, which is difficult to calculate for newly installed apps. To address these limitations, we initially trained our prediction model using data collected from a single display during the first phase, and re-trained the prediction model using data collected from the prototype of a dual display device, which we developed along with LG Electronics, during the second phase. In addition, we applied an app prediction user interface to the prototype. Next, we applied a stacked long–short term memory architecture, a type of sequential deep learning architecture, to train the prediction model without calculating the transition probability. The experiment results show that our proposed method achieves an effective app usage prediction for dual display devices in a qualitative way, and outperforms all other approaches in quantitative experiments with top-3 and top-5 level accuracies. The reason for the higher performance of the proposed method is that it captures the real usage of a dual display and reflects multitasking use in its app usage prediction. In addition, we also verified the effects of a few variables such as the location, time, and two-phase training.