Context-Aware Interactive Content Adaptation Iqbal Mohomed, Jim Cai, Sina Chavoshi, Eyal de Lara...

Context-Aware Interactive Content Adaptation

Iqbal Mohomed, Jim Cai, Sina Chavoshi, Eyal de LaraDepartment of Computer Science

University of Toronto

MobiSys2006

Need for Content Adaptation

• Mobile Devices have limited resources• Screen real-estate• Networking• Battery Life• User Interface• Memory• Processing Capability

Factors to Consider

• Content Usage Semantics

Factors to Consider

• Content Usage Semantics

• Context

WebServer

AdaptationProxy

URICA (EuroSys2006)

WebServer

AdaptationProxy

URICA (EuroSys2006)

Prediction

WebServer

AdaptationProxy

URICA (EuroSys2006)

Feedback

Prediction

Predictions based on History

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Image Fidelity

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• Multiple Usages

• Multiple Context0

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Challenge: Ambiguity in Feedback

Challenge: Ambiguity in Feedback

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Content

Group based on Context

Content

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How?• A lot of context can differ across users

• E.g., Display size, Network Connectivity, Location, etc.

• Influential context can vary across content and type of adaptation• Cannot group users by fixed set of context characteristics

• Grouping based on all possible combinations of context is infeasible• Results in many groups, each with few members• Significant overhead maintaining many groups• Long time until convergence of predictions within groups

Contributions

• User feedback is used to identify context that influences adaptation requirements

• Group users into communities based on influential context

• Predictions for each community are made on the restricted history of its users

Feedback-driven Context Selection (FCS)

• All users are grouped together initially• System tracks adaptation history for different

contexts• We conduct a “profiling experiment”

when there is sufficient history• Would users have benefited if they were grouped

separately based on some context?• If so, split original group based on this context

Users 1 2 3 4 5 6

Desired Adaptation 4 9 4 5 8 9

Users 1 2 3 4 5 6


To Split or Not to Split …

Users 1 2 3 4 5 6


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PredictionUsingMeanPolicy


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Users 1 2 3 4 5 6


Distance From Prediction 3 2 3 2 1 2


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Users 1 2 3 4 5 6


Distance From Prediction 3 2 3 2 1 2

Average Distance: 2.17


Users 1 3 4

Desired Adaptation 4 4 5

Users 2 5 6


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Distance From Prediction 0 0 1

Users 2 5 6



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Users 1 3 4



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Users 1 3 4



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Overall Average Distance: 0.33


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Average Distance: 2.17 VS


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Average Distance: 2.17 VS


It Depends!

Initial SituationAll Users in Same Community

Storage Requirements

Prediction Histogram



Context Grouping Histograms

Context A Context B


Context A Context B


Number of Histograms: 1 + 5 = 6


Prediction Histogram Prediction Histogram Prediction Histogram

Context GroupingHistograms



Context B Context B Context B


Context B Context B Context B


Number of Histograms: 3 + (2 x 3) = 9Increase of 50%

Splitting Costs Memory

Number of Histograms

# Contexts # Context Values

Before Any Splits

After Initial Split

Increase

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Before Any Splits

After Initial Split

Increase

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Before Any Splits

After Initial Split

Increase

3 3 10 21 11

5 5 26 105 79

10 10




Before Any Splits

After Initial Split

Increase

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Before Any Splits

After Initial Split

Increase

3 3 10 21 11

5 5 26 105 79

10 10 101 910 809

Splitting Can Be Expensive!

Splitting Economically• As long as space is available, splits can occur

• When system runs low on space, a score metric is used to rate splits

• Score is a function of:• Performance gain of a split• Access rate of object• Space Usage

• Splits with the highest scores are allowed to exist• Low score groups are merged together

Prototypes

• We developed two prototype systems:

• Page Layout Adaptation Prototype• Adapts display size and layout of images on

web pages for limited screen real-estate

• Fidelity Adaptation Prototype:• Adapts image quality to save bandwidth

consumption

User Studies• Goal was to capture desired adaptation of users

• During study, prediction component of prototypes was disabled

• Users adapt content to accomplish a specified task

• Traces used to conduct profiling experiments offline

Context Varied

• Page Layout Adaptation• Screen Size: SmartPhone, PDA, GBook

• Fidelity Adaptation• Network Bandwidth: 56Kbps, 100Mbps• Screen Size: PDA, Laptop• Location: 3 locations

Screen Size can matter …

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SmartPhone PDA GBook

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Result from Page Layout Adaptation Experiment

Screen Size can matter …

Grouping Users by Screen Size Improves Distance By 32%Splitting Probably Worthwhile

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Screen Size can matter … or it may not!

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Result from Fidelity Adaptation Experiment

Screen Size can matter … or it may not!

Grouping Users by Screen Size Improves Distance By 6%Splitting Probably Not Worthwhile

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Conclusions

• Influence of context on adaptation requirements can vary based on the content being adapted, as well as type of adaptation being performed

• We provide a technique that uses feedback to automatically determines influential context

• We show that grouping users based on context can lead to significant improvements in overall performance

Future Work

• Ultimate Goal: System adapts content based on:• Context, and • Usage semantics

• We are extending technique to also take usage semantics into account when adapting content

Future Work

• Ultimate Goal: System adapts content based on:• Context, and • Usage semantics

• We are extending technique to also take usage semantics into account when adapting content

Questions?

Context-Aware Interactive Content Adaptation Iqbal Mohomed, Jim Cai, Sina Chavoshi, Eyal de Lara...

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