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What Users Want

What Users Want. Daniel Weld University of Washington. Variation. 4x. 625x. Two Interface Trends . Usage. Need Increased Customization Beyond changing buttons on the toolbar Overriding inappropriate adaptation High-level functionality Programming by demonstration.

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What Users Want

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  1. What Users Want Daniel Weld University of Washington

  2. Variation 4x 625x Two Interface Trends Usage Daniel S. Weld / Univ. Washington

  3. Need Increased Customization • Beyond changing buttons on the toolbar • Overriding inappropriate adaptation • High-level functionality • Programming by demonstration “Steelcase-Inspired Software” -- David Gelernter • One Size Fits All • Need Increased Adaptivity • Beyond inconsistent defaulting • Adapt to available devices, connectivity, … • Adapt to user location • Adapt to user tasks & goals • Adapt to user calendar & current time (Some overlap with Contextual Computing) Daniel S. Weld / Univ. Washington

  4. Adaptivity & Customization • Deep Deployment ~ OS layer • Consistent Across Applications • Adaptation in every ‘dialog’ • Bridging Applications • Data gathering • Transformations Daniel S. Weld / Univ. Washington

  5. High-level Customization Pure Adaptation Outline • Motivation • Deliberative Software Agents • Programming by Demonstration • Adaptive Websites • Adaptive User Interfaces Daniel S. Weld / Univ. Washington

  6. Genesis: Internet Softbots [Etzioni & Weld CACM 94] • Interface Principles: • Goal-Oriented • Integrated • Balanced • Safe • User says what she wants • Agent determines how & whento achieve it •  planning-basedsoftbots Single, expressive & uniform interface E.g., printing files vs. FTPing them DT-lite: “Softbot must balance the cost of finding information on its own with the nuisance of asking the user.” Asimov’s first law… Safety, tidiness & thriftiness constraints in planner Daniel S. Weld / Univ. Washington

  7. Softbot Project Observations Rodney BargainFinder ILA Simon MetaCrawler Grouper Ahoy Info Manifold • Natural language interfaces • Popescu et al. • Yates et al. • Programming by demonstration ShopBot Occam Wrapper Induction } LSD Wed 10:15 Razor Mulder … Tukwila GLUE Piazza Gplex UW Softbot Family Tree • Specifying goals is bottleneck • Logical spec. language ?!$@#! • Forms interface • Limited to anticipated use • Not scalable • Often easier to just do task! • Except: data integration tasks Daniel S. Weld / Univ. Washington

  8. High-level Customization Pure Adaptation Outline • Motivation • Deliberative Software Agents • Programming by Demonstration • Adaptive Websites • Adaptive User Interfaces Daniel S. Weld / Univ. Washington

  9. Programming by Demonstration [Lau & Weld IUI-99] [Wolfman et al. IUI-01] • If it’s too hard for users to specify goals • Let’s watch them… • And try to help • Like plan recognition • Initial domains: • Email • Text editing • Cross domain [Lau et al. ICML-01] Daniel S. Weld / Univ. Washington

  10. Gentle-Slope Systems C Click’nCreate (Multimedia Fusion) VBasic Hypercard MFC C C Plugins kCmds Difficulty of use Hypertalk Ideal Basic Sophistication of what can be created Adapted from Myers et al. Daniel S. Weld / Univ. Washington

  11. VBasic Turing Cplt Robust Programing None Previous PBD Little Varying Varying Lots SmartEdit SmartPython On / Off Loops,Cond Robust Minimal Objectives Demand on user Expressiveness Domain engr Robustness Macro Record None On / Off Straight Ln Brittle Daniel S. Weld / Univ. Washington

  12. SmartEdit 1 [Lau et al. ICML-01] Daniel S. Weld / Univ. Washington

  13. 2 Daniel S. Weld / Univ. Washington

  14. 3 Daniel S. Weld / Univ. Washington

  15. 4 Daniel S. Weld / Univ. Washington

  16. 5 Daniel S. Weld / Univ. Washington

  17. 6 Daniel S. Weld / Univ. Washington

  18. Move to next <!-- Delete to next --> PBD as a learning problem • Action is function : input state ® output state • Editor state: text buffer, cursor position, etc. • Actions: move, select, delete, insert, cut, paste,… • Given a state sequence, infer actions • Many actions may be consistent with one example • Challenge: Weak bias + low sample complexity Daniel S. Weld / Univ. Washington

  19. Version Space Algebra • Version space = set of complex functions • Define version space hierarchically • Combine simpler version spaces with algebraic operators • Union È: analogous to set union • Join : cross product with consistency predicate • Transform: convert functions to different types • Can factor a version space Daniel S. Weld / Univ. Washington

  20. … … … … SMARTedit's version space Daniel S. Weld / Univ. Washington

  21. Action Move Paste Insert Copy Cut Select Delete Fast Consistency • Test consistency of example against entire version space • Quickly prune subtrees • Innovations: • Independent join allows BSR representation Daniel S. Weld / Univ. Washington

  22. Preliminary User Study • 6 undergrad CS majors • 7 repetitive tasks with & later w/out SMARTedit • Tasks: 4 to 27 iterations, 1-5 min to complete • Evaluation metrics: • Time saved completing task with SMARTedit's help • % user actions (keyboard + mouse) saved • User feedback Daniel S. Weld / Univ. Washington

  23. Time saved using SMARTedit Time (sec) Cost Saved Six Users X X Task Number Daniel S. Weld / Univ. Washington

  24. Action Savings with SMARTedit Six Users Percent of Actions Cost Saved XXXX X X Task Number Daniel S. Weld / Univ. Washington

  25. Observations from PBD • Overhead of macro recorder UI is high • Most repetitive tasks short • How many shell scripts do you write / day? • Focus on pure adaptivity • E.g., automatic segmentation Daniel S. Weld / Univ. Washington

  26. High-level Customization Pure Adaptation Outline • Motivation • Deliberative Software Agents • Programming by Demonstration • Adaptive Websites • Adaptive User Interfaces Daniel S. Weld / Univ. Washington

  27. Early Adaptation: Mitchell,Maes • Predict: Email message priorities Meeting locations, durations • Principle 1: Defaults minimize cost of errors • Principle 2: Allow users to adjust thresholds Daniel S. Weld / Univ. Washington

  28. Adaptation in Lookout: Horvitz Adapted from Horvitz Daniel S. Weld / Univ. Washington

  29. Resulting Principles [Horvitz CHI-99] • Decision-Theoretic Framework • Graceful degradation of service precision • Use dialogs to disambiguate (Considering cost of user time, attention) Adapted from Horvitz Daniel S. Weld / Univ. Washington

  30. Principles About Invocation • Allow efficient invocation & dismissal • Timeouts minimize cost of prediction errors Daniel S. Weld / Univ. Washington

  31. Adapting to Small Screens Daniel S. Weld / Univ. Washington

  32. Visitor Proteus Web server Web Site Adaptation in Proteus [Anderson et al. WWW-01] • Architecture • Personalizing in two steps: 1. Learn model of visitor from access logs • Transform content per learned model • Hill-climbing thru space of websites • Transforms: shortcuts & elision • Decision-theoretic guidance Daniel S. Weld / Univ. Washington

  33. Guiding the Search • Expected utility based on model of visitor • Model learned by mining server access logs • Sum value of each screen of each page • Discount by difficulty of reaching screen from p • Depends on how manylinks followed and howmuch scrolling required = p Daniel S. Weld / Univ. Washington

  34. Proteus Empirical Study • Observe real users on the desktop • Info-seeking goals drawn from random distribution • Personalize based on observations • Measure performance on mobile device • Number of links and scrolls, amount of time • Compare unmodified and personalized sites • Half users did unmodified first, others vice versa Daniel S. Weld / Univ. Washington

  35. Average number links followed Daniel S. Weld / Univ. Washington

  36. Analysis of Proteus • Why Proteus worked well • Suggested useful shortcuts • Elided mostly unnecessary content • Why Proteus worked poorly • Sometimes elided useful content • Users didn’t find shortcut, tho it existed • Flaws with implementation more than concept Daniel S. Weld / Univ. Washington

  37. Principles • Saliency of new UI operations is crucial • How name shortcuts? • Eliminating features is dangerous • Must partition dynamicity • Maintain separate dynamic & static “areas” • Always allow previous navigational methods • Duplicate functionality if necessary • Accurate prediction also crucial Daniel S. Weld / Univ. Washington

  38. Partitioned Dynamism Daniel S. Weld / Univ. Washington

  39. Partitioned Dynamism Daniel S. Weld / Univ. Washington

  40. Partitioning Failure Daniel S. Weld / Univ. Washington

  41. Principles • Must partition dynamicity • Accurate prediction also crucial Daniel S. Weld / Univ. Washington

  42. Predicting User Behavior [Anderson et al. IJCAI-01] • Model as Sequential Process • Markov Models • Mixtures of Markov Models • Second-Order… • Conditioning on Position in Trace • Etc. # times sd was followed P(sd) = Total # visits to s Daniel S. Weld / Univ. Washington

  43. Weakness of Markov Models • Each state is trained independently • Abundant training data at one state cannot improve prediction at another state • Large state models require vast training data • Problematic since Web trace data is sparse • A single visitor views ~0% of any site • New & dynamic content not in training data Daniel S. Weld / Univ. Washington

  44. DPRM RMM Reasoning about Uncertainty PRM Bayes Net DBN Structure Relational Sequence MM Daniel S. Weld / Univ. Washington

  45. Relational Markov Models [Anderson et al. KDD02] • Domains often contain relational structure • Each state is a tuple in relational DB sense • Structure enables state generalization • Which allows learning from sparse data ProductPage ProductName StockLevel Apple_iMac in_stock Palm_m505 backorder Daniel S. Weld / Univ. Washington

  46. D: a set of hierarchical domains Defn: Markov Model Relational • Q: set of states • Pages in a web site • Each state ~ a relation ProductPage(Apple_iMac, in_stock) • p: init prob distribution • A: transition probability matrix • R: a set of relations Daniel S. Weld / Univ. Washington

  47. AllProducts … AllComputers … AllPDAs AllDesktops … AppleDesktops … … iMac … Domain Hierarchies ProductName Instance of relation with leaf values is a state, e.g. ProductPage(iMac, in_stock) Daniel S. Weld / Univ. Washington

  48. AllProducts … AllComputers … AllPDAs AllDesktops … AppleDesktops … … iMac … Domain Hierarchies ProductName Instance of relation with non-leaf values is a set of states: an abstraction, e.g. ProductPage(AllComputers, in_stock) Daniel S. Weld / Univ. Washington

  49. RMM MainEntryPage() ProductPage(AllProducts, AllStockLevels) CheckoutPage() … ProductPage(AllProducts, backorder) … ProductPage(AllProducts, instock) E-commerce Site: Markov Ver. m505_backorder.html main.html checkout.html iMac_instock.html dell4100_instock.html Daniel S. Weld / Univ. Washington

  50. s  d s d RMM generalization • Want to estimate P(sd) … but no data! • Use shrinkage • Can do this with abstractions of d and s • Let  be an abstraction of s and  of d   ? Daniel S. Weld / Univ. Washington

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