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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