A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Deciphering mobile search patterns: a study of Yahoo! mobile search queries
Proceedings of the 17th international conference on World Wide Web
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis on repeat-buying patterns
Knowledge-Based Systems
Predicting human behaviour from selected mobile phone data points
Proceedings of the 12th ACM international conference on Ubiquitous computing
An effective approach for mining mobile user habits
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
By their apps you shall understand them: mining large-scale patterns of mobile phone usage
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Mobile search pattern evolution: the trend and the impact of voice queries
Proceedings of the 20th international conference companion on World wide web
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Location-aware click prediction in mobile local search
Proceedings of the 20th ACM international conference on Information and knowledge management
Hybrid models for future event prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Smartphone usage in the wild: a large-scale analysis of applications and context
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
Is this app safe?: a large scale study on application permissions and risk signals
Proceedings of the 21st international conference on World Wide Web
Fast app launching for mobile devices using predictive user context
Proceedings of the 10th international conference on Mobile systems, applications, and services
GetJar mobile application recommendations with very sparse datasets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding and prediction of mobile application usage for smart phones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Mining Temporal Profiles of Mobile Applications for Usage Prediction
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
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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 MinEntropy. 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 MinEntropy 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.