Feature fusion for mobile usage prediction using rank-score characteristics

The aim of this paper is to investigate feature fusion problem for mobile usage prediction using combinatorial fusion analysis (CFA). CFA uses the rank-score characteristics (RSC) function to guide the process of selecting score-based fusion (SF) or rank-based fusion (RF). We study the feature fusion of two mobile adaptive user interface applications: dynamic shortcuts for application launching and dynamic contact list, which improve the usability of mobile devices through usage predication. Our results confirm that for mobile usage prediction RSC function is useful for feature fusion decision. It also proves that RF outperforms SF when the features have unique scoring behavior and relatively high performance.

[1]  D. Frank Hsu,et al.  Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval , 2005, Information Retrieval.

[2]  Jie Liu,et al.  Fast app launching for mobile devices using predictive user context , 2012, MobiSys '12.

[3]  Yang Wang,et al.  AppRush: Using Dynamic Shortcuts to Facilitate Application Launching on Mobile Devices , 2013, ANT/SEIT.

[4]  Chuan Yi Tang,et al.  Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction , 2007, IEEE Transactions on NanoBioscience.

[5]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[6]  Soon Myoung Chung,et al.  Combining Multiple Feature Selection Methods for Text Categorization by Using Rank-Score Characteristics , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[7]  Damian M. Lyons,et al.  Combining multiple scoring systems for target tracking using rank-score characteristics , 2009, Inf. Fusion.

[8]  Andreas Komninos,et al.  Patterns of usage and context in interaction with communication support applications in mobile devices , 2012, Mobile HCI.

[9]  D. Frank Hsu,et al.  Rank-Score Characteristics (RSC) Function and Cognitive Diversity , 2010, Brain Informatics.

[10]  Guanling Chen,et al.  AppJoy: personalized mobile application discovery , 2011, MobiSys '11.

[11]  Clayton Shepard,et al.  LiveLab: measuring wireless networks and smartphone users in the field , 2011, SIGMETRICS Perform. Evaluation Rev..

[12]  Seng Wai Loke,et al.  Adapting the mobile phone for task efficiency: the case of predicting outgoing calls using frequency and regularity of historical calls , 2011, Personal and Ubiquitous Computing.

[13]  D. Lazer,et al.  Inferring Social Network Structure using Mobile Phone Data , 2006 .

[14]  Hidetoshi Ueno,et al.  Automatic mobile menu customization based on user operation history , 2009, Mobile HCI.

[15]  Sang Lyul Min,et al.  On the existence of a spectrum of policies that subsumes the least recently used (LRU) and least frequently used (LFU) policies , 1999, SIGMETRICS '99.