Name
Abstract | ||
|---|---|---|
In this paper, we examine the potential of using mobile context to model user engagement. Taking an experimental approach, we systematically explore the dynamics of user engagement with a smartphone through three different studies. Specifically, to understand the feasibility of detecting user engagement from mobile context, we first assess an EEG artifact with 10 users and observe a strong correlation between automatically detected engagement scores and user's subjective perception of engagement. Grounded on this result, we model a set of application level features derived from smartphone usage of 10 users to detect engagement of a usage session using a Random Forest classifier. Finally, we apply this model to train a variety of contextual factors acquired from smartphone usage logs of 130 users to predict user engagement using an SVM classifier with a F1-Score of 0.82. Our experimental results highlight the potential of mobile contexts in designing engagement-aware applications and provide guidance to future explorations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2971648.2971760 | UbiComp |
Keywords | Field | DocType |
Mobile Sensing, Engagement, Behaviour Modelling, EEG | Mobile sensing,Mobile context,Computer science,User engagement,Human–computer interaction,Svm classifier,Random forest,Multimedia,Perception | Conference |
Citations | PageRank | References |
8 | 0.46 | 35 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akhil Mathur | 1 | 101 | 15.10 |
| Nicholas D. Lane | 2 | 4247 | 248.15 |
| Fahim Kawsar | 3 | 909 | 80.24 |