This document summarizes a research study that evaluated call availability prediction using a large dataset of 31,311 calls from 418 users. The study found call availability could be predicted with 83% accuracy using 15 basic features like time of last activity and screen status. Prediction was more accurate (87%) with personalized models. The top 5 strongest predictors were features related to time of last activity and screen status. The study demonstrated call availability prediction could be used to mute ringtones when unavailable or let callers check availability.

1. Research
Large-Scale
Evaluation of Call-
Availability
Prediction
Martin Pielot,
Telefónica Research
ACM UbiComp’14, Sep, 2014, Seattle, USA
Wed, Sep 17, 2014 – 14:00 – 15:30
Session: Interruptability & Notifications
"2007Computex e21Forum-MartinCooper" by
Rico Shen. Via Wikimedia. CC-SA 3.0.

2. Phone calls reach us
anywhere,
anytime
makelessnoise. Phon-ey Call. via Flickr, Jul 07, 2006 (CC BY 2.0)

3. 30%
of all calls
are missed
WilliamTheaker. Hamhung cyclist. via Wikipedia, Apr 17, 2012 (CC

4. Please raise hands:
Who of you would take a call
right now?
Kaz. Hände. Via Pixabay, Nov 26, 2013. (Public Domain

5. Interruption!

6. Callers want to know:
 Location and time,
 physical, social,
emotional availability,
 and current activity.
De Guzman et al. 2007
"White Diamonds Party" by Club Skirts Dinah Shore Weekend - Own work. via Wikimedia Commons. CC BY-SA
3.0 -

7. Callers want to know:
 Location and time,
 physical, social,
emotional availability,
 and current activity.
De Guzman et al. 2007
"White Diamonds Party" by Club Skirts Dinah Shore Weekend - Own work. via Wikimedia Commons. CC BY-SA
3.0 -
Callees react depend.
on:
 Location and time,
 Presence of
others,
 and current activity.
Danninger et al. 2006

8. People have concerns sharing too much
contextual information Knittel et al. 2011
"No trespassing" by Djuradj Vujcic - Own work. via Wikimedia Commons. CC BY-SA
3.0.

9. Availability
Location
Time
Presence of others
Current Activity
…
Machine-Learning
User Model
…

10. Related Approaches
Horvitz et al.
2005
Using calendar details from Outlook to
predict cost of interruption by call

11. Related Approaches
Horvitz et al.
2005
Using calendar details from Outlook to
predict cost of interruption by call
Rosenthal et
al. 2011
Use ESM to train phones to mute ringer in
certain situations

12. Related Approaches
Horvitz et al.
2005
Using calendar details from Outlook to
predict cost of interruption by call
Rosenthal et
al. 2011
Use ESM to train phones to mute ringer in
certain situations
Pejovic and
Musolesi 2014
Identifying opportune moments for mobile
device-based interruptions

13. Opportunities to Advance Line of Work
(1)Actual reaction
(1) Sample selection & size

14. Study

16. On shake

17. Anonymous
logs

18. Sep 17, 2014, 15:20
Screen
Status
Reaction to
the call
Proximity
Sensor
Day and
Time
Ringer
mode
Charging

19. Results

20. 31,311 calls
from 418 distinct user

21. Extracted 15 Basic Features
Category Feature
Last Active Last ringer change (time)
Last Active Last screen change (time)
Last Active Last (un)plugged (time)
Last Active Last call (time)
Currently Active Screen status
Currently Active Pitch of phone
Relationship How often called by caller
Context Day of the week
Context Hour of the day
Context Charger (un)plugged
Context Ringer mode
Context Last call silenced
Context Activity / Acceleration
Context Screen (not) covered
Context Last call picked

22. Prediction
Random Forest (10 trees)
Classes: available | not available
Accuracy 83.2% (κ=.646)
(10-fold cross-validation)

23. 19,175 calls picked up
61.2% baseline
accuracy

24. Advantages of large data set

25. Model accuracy over time
85
83
81
79
77
75
73
71
Accuracy (%)
Number of instances (phone calls)

26. Random Forest Model
Personalized model
Subset of 120 calls: 87.0% (κ=.640)

27. Features Ranked by Prediction Power
Category Feature Mean Rank
Last Active Last ringer change (time) 1
Last Active Last screen change (time) 2
Currently Active Screen status 3.6
Last Active Last (un)plugged (time) 5.4
Last Active Last call (time) 6.8
Context Activity / Acceleration 7.3
Relationship How often called by caller 7.6
Context Day of the week 9.4
Context Hour of the day 10
Context Charger (un)plugged 10.1
Context Ringer mode 11.4
Context Last call silenced 12.4
Currently Active Pitch of phone 12.5
Context Screen (not) covered 13
Context Last call picked 14.1

28. Application

29. Mute the ringer

30. Communicate
Non-Availability

31. Communicate
Non-Availability Likely to be
unavailable

32. Large-Scale Evaluation of Call-Availalability Prediction.
First large-scale study (31,311 calls) of call-availability
prediction
Prediction possible with 15 basic features
83% accuracy (generic models)
87% accuracy (personalized models)
Strongest 5 predictors
4 features regarding time of last activity
Screen status
Use cases
Mute ringer on unavailability
Allow caller to check availability
Large-Scale
Evaluation of Call-
Availability
Prediction
Martin Pielot,
Telefónica Research
martin.pielot@telefonica.com
ACM UbiComp’14,
Sep, 2014, Seattle, USA
Wed, Sep 17, 2014 – 14:00 –
15:30
Interruptability & Notifications
Q&A