Roughly 1/3 of all phones calls are not picked up. With this work, we explored whether the called phone can know in advance, whether its user is likely to pick up a call. This would allow to, amongst other things, communicate (non)availability in advance to the call or trigger intelligent muting.
This work shows that mobile phones can predict with an accuracy of 83.2% whether its user will accept an incoming phone call or not. When personalizing those models, the accuracy can be increased to 87%.Therefore, the phone needs to keep track of 15 features, such as the time since the last call, the day of the week, or the ringer mode. The 5 strongest predictors are:
(1) time since the last ringer mode change,
(2) time since the screen was last turned on or off,
(3) screen status (on/off),
(4) time since the phone was last (un)plugged, and
(5) time since the last call.
These findings show that it is possible to create an automated availability status for phone calls. Integrated into any phone call application, it could help to manage expectations by sharing the availability prediction with potential callers, and through that greatly impact the overall user experience. Further, knowing whether is a user is likely to take a call might be useful to intelligently allocate resources in a multi-device messenger environment.
To obtain the necessary data, we instrumented a previously-developed application called Silencer with anonymous data-collection facilities. During a two-month period, the app logged how 418 users reacted to 31311 phone calls. Alongside each call, the above mentioned 15 features were collected. Using a Random Forest, we computed the accuracy of a generic model, of personalized models with different numbers of calls (in average, 50 or more calls are needed to outperform the generic model – so it should be very quick to generate accurate personalized models), and to determine the prediction strength of each feature.
This research is described in detail in the paper Large-Scale Study of Call-Availability Prediction, which will be presented at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, held in September 2014 in Seattle, USA.
