Conditional Log-linear Models for Mobile Application Usage Prediction.

Over the last decade, mobile device usage has evolved rapidly from basic calling and texting to primarily using applications. On average, smartphone users have tens of applications installed in their devices. As the number of installed applications grows, finding a right application at a particular moment is becoming more challenging. To alleviate the problem, we study the task of predicting applications that a user is most likely going to use at a given situation. We formulate the prediction task with a conditional log-linear model and present an online learning scheme suitable for resource-constrained mobile devices. Using real-world mobile application usage data, we evaluate the performance and the behavior of the proposed solution against other prediction methods. Based on our experimental evaluation, the proposed approach offers competitive prediction performance with moderate resource needs.