Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Contextual patterns in mobile service usage
Personal and Ubiquitous Computing
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Adaptive Distances on Sets of Vectors
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
AppJoy: personalized mobile application discovery
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Proceedings of the 13th international conference on Ubiquitous computing
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Exploiting Social Networks for Large-Scale Human Behavior Modeling
IEEE Pervasive Computing
Smartphone usage in the wild: a large-scale analysis of applications and context
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Multi-instance Metric Learning
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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
USENIX ATC'12 Proceedings of the 2012 USENIX conference on Annual Technical Conference
Understanding and prediction of mobile application usage for smart phones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
Smartphone App Use Among Medical Providers in ACGME Training Programs
Journal of Medical Systems
Hi-index | 0.00 |
Reliable smartphone app prediction can strongly benefit both users and phone system performance alike. However, real-world smartphone app usage behavior is a complex phenomena driven by a number of competing factors. In this pa- per, we develop an app usage prediction model that leverages three key everyday factors that affect app usage decisions -- (1) intrinsic user app preferences and user historical patterns; (2) user activities and the environment as observed through sensor-based contextual signals; and, (3) the shared aggregate patterns of app behavior that appear in various user communities. While rapid progress has been made recently in smartphone app prediction, existing prediction models tend to focus on only one of these factors. We evaluate a multi-faceted approach to prediction using (1) a 3-week 35-user field trial, along with (2) analysis of app usage logs of 4,606 smartphone users worldwide. We find our app usage model can not only produce more robust app predictions than conventional techniques, but it can also enable significant smartphone system optimizations.