On mining mobile apps usage behavior for predicting apps usage in smartphones

Predicting Apps usage has become an important task due to the proliferation of Apps, and the complex of Apps. However, the previous research works utilized a considerable number of different sensors as training data to infer Apps usage. To save the energy consumption for the task of predicting Apps usages, only the temporal information is considered in this paper. We propose a Temporal-based Apps Predictor (abbreviated as TAP) to dynamically predict the Apps which are most likely to be used. First, we extract three Apps usage features, global usage feature, temporal usage feature, and periodical usage feature from the Apps usage trace. Then, based on those explored features, we dynamically derive an Apps usage probability model to estimate the current usage probability of each App in each feature. Finally, we investigate the usage probability in each feature and select k Apps with highest usage probability from the probability model. In this paper, we propose two selection algorithms, MaxProb and Min Entropy. To evaluate the performance of TAP, we use two real mobile Apps usage traces and assess the accuracy and efficiency. The experimental results show that the proposed TAP with the Min Entropy selection algorithm could have shorter response time of Apps prediction. Moreover, the accuracy reaches to 80% when k is 5, and when k is 7, the accuracy achieves almost 100% in both of the two real datasets. Copyright is held by the owner/author(s).