1. 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

2. 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!

3. Automatic Adaptation Unmodified Content Server Adaptation ProxyMobile Device We Have The Mechanism But … The Hard Problem is Policies!

4. 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

5. 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!

6. 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

7. Prediction 10KB 20KB Adaptation Proxy Mobile 1 How it Works Application Server 2 Server 1 Improve Fidelity Mobile 2 Application 40KB

8. Predictions based on History

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

13. Varying Adaptation Preferences Leads To “Dirty” History

14. 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!

17. Feedback!

19. 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

20. 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

21. 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

22. 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

23. Fidelity Adaptation Prototype Intended for bandwidth-limited environments

24. Fidelity Adaptation Prototype Intended for bandwidth-limited environments

25. Fidelity Adaptation Prototype Intended for bandwidth-limited environments

26. Fidelity Adaptation Prototype Intended for bandwidth-limited environments

27. 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

28. 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

29. Results From Movie Posters Study GMM (One-sided Feedback)

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

31. 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

32. 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

33. Operation of all-in Algorithm 1889324878 2879313889 3879223546 4324989111 Image avg, min, max Clusters Found In History Using history, precalculate clusters as well as range of fidelities (min,max) for each image

34. Operation of all-in Algorithm 18889324878 28879313889 38879223546 43324989111 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

35. Operation of all-in Algorithm 184889324878 283879313889 383879223546 431324989111 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

36. Operation of all-in Algorithm 184889324878 283879313889 383879223546 431324989111 Image Desired By User Provided By System avg, min, max Clusters Found In History

37. Operation of all-in Algorithm 184889324878 283879313889 383879223546 431324989111 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

38. Operation of all-in Algorithm 184889324878 283879313889 383879223546 431324989111 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

39. Operation of all-in Algorithm 188889324878 289879313889 386879223546 431324989111 Image Desired By User Provided By System avg, min, max Clusters Found In History

40. Operation of all-in Algorithm 188889324878 289879313889 386879223546 431324989111 Image Desired By User Provided By System avg, min, max Clusters Found In History

41. Operation of all-in Algorithm 188889324878 289879313889 386879223546 431324989111 Image Desired By User Provided By System avg, min, max Clusters Found In History

42. Operation of all-in Algorithm 188889324878 289879313889 386879223546 431324989111 Image Desired By User Provided By System avg, min, max Clusters Found In History When only a single cluster remains, serve at the average

43. Operation of all-in Algorithm 188889324878 289879313889 388879223546 433324989111 Image Desired By User Provided By System avg, min, max Clusters Found In History When only a single cluster remains, serve at the average

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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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?