correlation-based content adaptation for mobile web browsing

Correlation-Based Content Adaptation

For Mobile Web Browsing

Iqbal Mohomed, Adin Scannell, Nilton Bila, Jin Zhang, Eyal de Lara

Department of Computer Science

University of Toronto

Middleware 2007

Need for Adaptation

Network

Connectivity

(seconds)

1 Mbps (DSL) 5

3G (384Kbps) 12.5

GPRS (40Kbps) 120

Downloaded Data 600KB

Content must be customized!

Automatic Adaptation

Unmodified Content Server

Adaptation ProxyMobile Device

We Have The Mechanism But …

The Hard Problem is Policies!

Achieving Fine Grain Adaptation

• Usage and context both affect the customization that is needed

• Machines have a hard time distinguishing between good and bad adaptations

• People are better

Achieving Fine Grain Adaptation

• Usage and context both affect the customization that is needed

• Machines have a hard time distinguishing between good and bad adaptations

• People are better

Rely on a few users to adapt content for everyone!

Usage-awaRe Interactive Content Adaptation (URICA)

• Allow users to interactively refine system’s adaptation decision

• System learns from user modifications – Uses history for future adaptation predictions

• Applicable to a wide range of adaptation types, such as image fidelity and page layout

Prediction

10KB20KB

AdaptationProxy

Mobile 1

How it Works

Application

Server 2

Server 1Improve Fidelity

Mobile 2

Application

Predictions based on History

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Image Fidelity

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Image Fidelity

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Image Fidelity

Predictions based on History

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Image Fidelity

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Image Fidelity

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Image Fidelity

Challenge: When users have varying preferences, how do we pick an appropriate adaptation?

Varying Adaptation Preferences Leads To “Dirty” History

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Image Fidelity

Correlation-Based Content Adaptation

• Typically, web pages/sites contain multiple objects– e.g. images

• Use history to determine correlations in the adaptation requirements of different objects

• When a user provides corrective feedback for one object, update the adaptation prediction for all related objects!

Feedback!

How To Find Correlations Automatically?

• Boosted Decision Stumps– Mine data to create rules that capture relationships between the

adaptation requirements of objectsFor objects X and Y: IF X > 3 THEN Y=7

• Gaussian Mixture Model– History data is used to parameterize a set of Gaussian

distributions– Key parameter is # of distributions to consider– A user belongs to each distribution with some prior probability– As the user provides feedback to the system, these probabilities

are updated

Page Layout Adaptation Prototype• Intended for use on mobile devices with limited screen

real-estate• Allows users to increase or decrease the display size of

images on web pages• Key metric is # of user interactions required to reach

appropriate adaptation

Page Layout User Study

• User study– 3 simulated display sizes: Phone, PDA

and in-car browser– 4 web pages, 3 images per page– 30 participants

• No prediction during data collection• Traces used to run experiments

– Leave-one-out cross validation

Page Layout User Study

• User study– 3 simulated display sizes: Phone, PDA

and in-car browser– 4 web pages, 3 images per page– 30 participants

• No prediction during data collection• Traces used to run experiments

– Leave-one-out cross validation

Without CorrelationHistory-based predictions: 15 interactions, on average

With CorrelationsDecision Stumps: 5.1 interactions, on averageGaussian Mixture Model: 5.9 interactions, on average

Fidelity Adaptation Prototype

• Intended for bandwidth-limited environments

Fidelity Adaptation Prototype (contd.)

• Two primary metrics of concern– Number of user interactions– Wasted bandwidth

• Users can only increase the fidelity of images– Users have little incentive to reduce the fidelity of an

image that they have already been served– Feedback is only one-sided, as opposed to the two-

sided feedback received in page layout adaptation

Fidelity Adaptation:Movie Posters Study

• User study– Users given 1 of 3 tasks– 9 web pages, 1 image of a movie poster per

page– 37 participants per task

• No prediction during data collection

• Traces used to run experiments– Leave-one-out cross validation

Results From Movie Posters Study

GMM (One-sided Feedback)

GMM (Perfect Feedback, Hypothetical)

Gaussian Mixture Model and Decision Stumps Did Not Perform WellWhen Only One-Sided Feedback Is Available

Our Approach

• Run standard clustering algorithm

(K-Means) on adaptation history

• Custom algorithm (called all-in) to perform online classification

• Intuition: narrow down the possible clusters a user can belong to quickly

Operation of all-in Algorithm

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Image avg, min, max avg, min, max avg, min, max

Clusters Found In History

Using history, precalculate clusters as well as range of fidelities (min,max) for each image

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DesiredBy

ProvidedBy

System

avg, min, max avg, min, max avg, min, max

When a user initially accesses the page, all clusters are valid

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DesiredBy

ProvidedBy

System

For each image, serve the lowest maximum fidelity value from the valid clusters

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DesiredBy

ProvidedBy

System

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System

If the user requests a higher fidelity for an image, we can eliminate a cluster

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DesiredBy

ProvidedBy

System

If the user requests a higher fidelity for an image, we can eliminate a cluster

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DesiredBy

ProvidedBy

System

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DesiredBy

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System

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DesiredBy

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System

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When only a single cluster remains, serve at the average

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When only a single cluster remains, serve at the average

All-in

Fulfillment Time(Movie Posters Study)

Legend:

NA: No AdaptationDS: Decision StumpsGM: Gaussian MixtureAI: All-inOR: Hypothetical Oracle

Fulfillment Time = download time + time spent by user to provide feedback

Legend:

Summary

• Correlation-based adaptation can be used to provide fine grain customization of content even when users have varying preferences

• Standard machine learning techniques work well when there is two-sided feedback (e.g. page layout adaptation)

• All-in algorithm performs well when only one-sided feedback is available (e.g. fidelity adaptation)– All-in behaves aggressively to quickly narrow down

the number of clusters to which a user can belong

Summary

• Correlation-based adaptation can be used to provide fine grain customization of content even when users have varying preferences

• Standard machine learning techniques work well when there is two-sided feedback (e.g. page layout adaptation)

• All-in algorithm performs well when only one-sided feedback is available (e.g. fidelity adaptation)– All-in behaves aggressively to quickly narrow down

the number of clusters to which a user can belong

http://adaptive.cs.toronto.edu/

Questions?

correlation-based content adaptation for mobile web browsing

appropriate adaptation

wide range of adaptation

user modifications

considera user

user interactions

hard time distinguishing

mobile devices

worksadaptationproxy

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