Learning Mobile App Usage Routine through Learning Automata
This work addresses energy efficiency and user experience for smartphone users, but it is incremental as it applies an existing learning method to a specific domain problem.
The paper tackles the challenge of optimizing smartphone app launch time and battery preservation by predicting user app usage routines using Learning Automata, an online learning method that updates transition probabilities between app launches, and shows considerable success compared to two baseline methods in experiments using a real-world dataset.
Since its conception, smart app market has grown exponentially. Success in the app market depends on many factors among which the quality of the app is a significant contributor, such as energy use. Nevertheless, smartphones, as a subset of mobile computing devices. inherit the limited power resource constraint. Therefore, there is a challenge of maintaining the resource while increasing the target app quality. This paper introduces Learning Automata (LA) as an online learning method to learn and predict the app usage routines of the users. Such prediction can leverage the app cache functionality of the operating system and thus (i) decreases app launch time and (ii) preserve battery. Our algorithm, which is an online learning approach, temporally updates and improves the internal states of itself. In particular, it learns the transition probabilities between app launching. Each App launching instance updates the transition probabilities related to that App, and this will result in improving the prediction. We benefit from a real-world lifelogging dataset and our experimental results show considerable success with respect to the two baseline methods that are used currently for smartphone app prediction approaches.