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 Time (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 10KB 20KB Adaptation Proxy Mobile 1 How it Works Application Server 2 Server 1 Improve Fidelity Mobile 2 Application 40KB

Predictions based on History

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

Varying Adaptation Preferences Leads To “Dirty” History

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 objects For 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 Correlation History-based predictions: 15 interactions, on average With Correlations Decision Stumps: 5.1 interactions, on average Gaussian Mixture Model: 5.9 interactions, on average

Fidelity Adaptation Prototype Intended for bandwidth-limited environments

Fidelity Adaptation Prototype Intended for bandwidth-limited environments

Fidelity Adaptation Prototype Intended for bandwidth-limited environments

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)

Results From Movie Posters Study GMM (One-sided Feedback) GMM (Perfect Feedback, Hypothetical)

Results From Movie Posters Study GMM (One-sided Feedback) GMM (Perfect Feedback, Hypothetical) Gaussian Mixture Model and Decision Stumps Did Not Perform Well When 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 Image avg, min, max Clusters Found In History Using history, precalculate clusters as well as range of fidelities (min,max) for each image

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History When a user initially accesses the page, all clusters are valid

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History For each image, serve the lowest maximum fidelity value from the valid clusters

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History If the user requests a higher fidelity for an image, we can eliminate a cluster

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History If the user requests a higher fidelity for an image, we can eliminate a cluster

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History When only a single cluster remains, serve at the average

Operation of all-in Algorithm Image Desired By User Provided By System avg, min, max Clusters Found In History When only a single cluster remains, serve at the average

Results From Movie Posters Study GMM (One-sided Feedback) GMM (Perfect Feedback, Hypothetical) All-in

Fulfillment Time (Movie Posters Study) Legend: NA: No Adaptation DS: Decision Stumps GM: Gaussian Mixture AI: All-in OR: Hypothetical Oracle Fulfillment Time = download time + time spent by user to provide feedback

Fulfillment Time (Movie Posters Study) Legend: NA: No Adaptation DS: Decision Stumps GM: Gaussian Mixture AI: All-in OR: Hypothetical Oracle Fulfillment Time = download time + time spent by user to provide feedback

Fulfillment Time (Movie Posters Study) Legend: NA: No Adaptation DS: Decision Stumps GM: Gaussian Mixture AI: All-in OR: Hypothetical Oracle Fulfillment Time = download time + time spent by user to provide feedback

Fulfillment Time (Movie Posters Study) Legend: NA: No Adaptation DS: Decision Stumps GM: Gaussian Mixture AI: All-in OR: Hypothetical Oracle Fulfillment Time = download time + time spent by user to provide feedback

Fulfillment Time (Movie Posters Study) Legend: NA: No Adaptation DS: Decision Stumps GM: Gaussian Mixture AI: All-in OR: Hypothetical Oracle Fulfillment Time = download time + time spent by user to provide feedback

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 Questions?