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.
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.
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
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)
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
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
People are only willing to share some contextual information, such as current location, current activity, or presence of appointments.
[Knittel et al. 2011]
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"No trespassing by Djuradj Vujcic" by Djuradj Vujcic - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:No_trespassing_by_Djuradj_Vujcic.jpg#mediaviewer/File:No_trespassing_by_Djuradj_Vujcic.jpg
Be present in the lives of a large number of users
3,320, that is 10.8% of the calls where muted by shaking the phone and not answering the call
That is, they were most likely interruptive