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Carnegie Mellon
PMA: A Mobile Context-Aware Personal Messaging Assistant
Senaka ButhpitiyaDeepthi Madamanchi
Sumalatha KommarajuMartin Griss
CyLab Mobility Research
CenterMobility Research Center
Carnegie Mellon Silicon Valley
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Agenda
• Introduction to Email Sorting
• Related Work
• PMA – Design and Architecture
• Experiments & Results
• Conclusion
• Future Work
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What is a “Mobile Context-Aware Personal Messaging
Assistant”?• An advanced rule-based email management system
which uses the mobile user’s context and email content to• classify emails• prioritize emails• selectively deliver key messages to mobile phone
• Uses real-time context information from:• hard sensors (GPS, accelerometer, etc.) on Mobile
phone• soft sensors (calendar, …)
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Email Flooding in the Real World
Busy professionals receive in excess of 50 emails per day,23% require immediate attention
13% require attention later
64% are unimportant
Problem is even worse for mobileprofessionalsDifficult to sort through emails on mobile devices
Wastes precious bandwidth and battery life
End Result:Wastes time sorting through unwanted emails
Drastic reduction in productivity!
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Problems
• Most email sorting/classification programs take only email-content into account• Depending on users’ contexts, the emails that
they wish to see vary• Depending on the users’ contexts the number
of emails they can scan through varies
• Email sorting/classification programs consider importance only
Importance and urgency are orthogonal yet affects email sorting equally
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Unimportant Important
Non-Urgent
Evite for a BBQ.
From manager: Client visit pushed back by another
month.
Urgent Online auction: you were out bid.
Son missed his bus, pick him up from
school.
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PMA Architecture
PMA separately rates emails according importance and urgency using context information and email content
e.g. – email from the user’s boss about present meeting is important and very urgent
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Email-Preprocessor
Co
nte
xt
Gen
erat
or
Urgency Processor
Delivery Agent
Importance Processor
Context Data Emails
PMA decides on what-to deliver, how-to-deliver and where-to-deliveraccording to user’s context
e.g. – deliver as SMS, text-to-voice SMS, forward to co-worker
Uses a rule-based system for decision making
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Context Information
Gathered from hard sensors on a Nokia N95 (which also doubles as a delivery point for selected emails)
Gathered from soft sensors such as Google Calendar
Context includes all information related to user including,
• Static context such as name andfamily details
• Dynamic context such as meetingtopic, driving speed
• User preferences
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Experiment - 1
AIM – Test effectiveness of PMA’s urgency and importance classifiers
For various user contexts,• PMA classifies a test set of emails separately for importance
and urgency• compared against ratings for the same emails by user
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Number of type X emails correctly classified by PMAPrecision = Number of emails classified by PMA as X
Number of type X emails correctly classified by PMA Recall = Total number of emails selected by users as type X
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Summary of precision and recall of importance classification
Summary of precision and recall of urgency classification
Results
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Random PMA
Recall 33.3% 96.3%
Precision 26.1% 88.2%
Random PMA
Recall 8.3% 94.8%
Precision 8.3% 92.6%
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Experiment - 2
AIM – Test effectiveness of PMA’s delivery agent and overall system
For various user contexts,• PMA decides on what action to perform with a given
email• SMS to user• Send to users as text-to-voice SMS• Folder for later viewing• Take no action
• compared against user’s expected action on each email
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Results
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Conclusions
PMA sorts and delivers messages that are relevant to the user in his current context, effectively
• Uses emails content and user’s context information for decision making
PMA uses separate scales to measure urgency and importance of an email
PMA is scalable for all inbox sizes
PMA is easily personalized to suit the requirements of any user for better accuracy
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Future Work
Performance of PMA• Machine learning schemes to automate the learning from
user feedback
• Improve run-time
Generalization of PMA• Support for various email accounts Yahoo! mail, Hotmail, etc.
• Support for additional message types (SMS, IM, RSS, etc.)
Personalization of PMA• User interface to create/edit custom rules
• Mobile device interface for feedback and usability
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Thank You
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