The active badge location system
ACM Transactions on Information Systems (TOIS)
Machine Learning
A Framework for Developing Mobile, Context-aware Applications
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Context-Aware SVM for Context-Dependent Information Recommendation
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mobile Situation-Aware Task Recommendation Application
NGMAST '08 Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies
A Recommendation Agent for Mobile Phone Users Using Bayesian Behavior Prediction
UBICOMM '09 Proceedings of the 2009 Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
Clustering and Naïve Bayesian Approaches for Situation-Aware Recommendation on Mobile Devices
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Unsupervised clustering of context data and learning user requirements for a mobile device
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Data transformation for sum squared residue
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Due to the large number of applications in the mobile phones, users usually go through a fixed menu hierarchy to find a specific interesting application. Hence, in our previous research, we realized the proactive mobile phone application recommendation using co-clustering and demonstrated the promising recommendation performance on a smartphone. The approach first autonomously extracts user's behavioral patterns from the usage log of user interactions with the device as well as environments and then recommends potential applications that might be interesting to the user at the corresponding specific situation. In this paper, as a follow-up to this novel platform of intelligent smartphone-based situation-awareness, we investigate sophisticated methodologies that lead to better performance. To achieve this goal, we considered various co-clustering algorithms with different data transformations and weighting schemes for simulated mobile phone usage data. Through non-exhaustive, but pretty comprehensive experimental setting, we find what specific co-clustering algorithms with what specific data transformations and weighting schemes improve accuracy performance in extracting specific user patterns.