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Context-Aware Interactive Content Adaptation

Explore the challenges of content adaptation for mobile devices and how user feedback drives context-based predictions, leading to efficient content delivery. Learn about feedback-driven context selection and the benefits of grouping users based on influential context factors. Discover the impact of splitting user groups for better predictions and storage requirements.

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Context-Aware Interactive Content Adaptation

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  1. Context-Aware Interactive Content Adaptation Iqbal Mohomed, Jim Cai, Sina Chavoshi, Eyal de Lara Department of Computer Science University of Toronto MobiSys2006

  2. Need for Content Adaptation • Mobile Devices have limited resources • Screen real-estate • Networking • Battery Life • User Interface • Memory • Processing Capability

  3. Factors to Consider • Content Usage Semantics

  4. Factors to Consider • Content Usage Semantics • Context

  5. URICA (EuroSys2006) Adaptation Proxy Web Server

  6. URICA (EuroSys2006) Adaptation Proxy Web Server

  7. URICA (EuroSys2006) Adaptation Proxy Web Server

  8. URICA (EuroSys2006) Adaptation Proxy Web Server Prediction

  9. URICA (EuroSys2006) Adaptation Proxy Web Server Prediction

  10. URICA (EuroSys2006) Adaptation Proxy Web Server Prediction

  11. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  12. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  13. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  14. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  15. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  16. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  17. URICA (EuroSys2006) Adaptation Proxy Web Server Feedback Prediction

  18. Predictions based on History

  19. Predictions based on History

  20. Predictions based on History

  21. Challenge: Ambiguity in Feedback • Multiple Usages • Multiple Context

  22. Challenge: Ambiguity in Feedback • Multiple Usages • Multiple Context

  23. Challenge: Ambiguity in Feedback Content

  24. Group based on Context Content

  25. 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

  26. 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

  27. 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

  28. To Split or Not to Split …

  29. To Split or Not to Split …

  30. To Split or Not to Split … Prediction Using Mean Policy

  31. To Split or Not to Split … Prediction Using Mean Policy

  32. To Split or Not to Split … Prediction Using Mean Policy Average Distance: 2.17

  33. To Split or Not to Split …

  34. To Split or Not to Split …

  35. To Split or Not to Split …

  36. To Split or Not to Split … Average Distance: 0.33 Average Distance: 0.33

  37. To Split or Not to Split … Overall Average Distance: 0.33

  38. To Split or Not to Split … VS Overall Average Distance: 0.33 Average Distance: 2.17

  39. To Split or Not to Split … VS Overall Average Distance: 0.33 Average Distance: 2.17 It Depends!

  40. Storage Requirements Initial Situation All Users in Same Community

  41. Storage Requirements Prediction Histogram

  42. Storage Requirements

  43. Storage Requirements Context Grouping Histograms

  44. Context A Context B Storage Requirements

  45. Context A Context B Storage Requirements Number of Histograms: 1 + 5 = 6

  46. Context A Context B Storage Requirements Number of Histograms: 1 + 5 = 6

  47. Storage Requirements Prediction Histogram Prediction Histogram Prediction Histogram Context Grouping Histograms Context Grouping Histograms Context Grouping Histograms

  48. Storage Requirements Context Grouping Histograms Context Grouping Histograms Context Grouping Histograms

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