CS376 Adaptive Interfaces

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Adaptive Interfaces
Jeffrey Heer 28 May 2009
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Final Project Presentations

• Tuesday June 9, 330-630pm, 104 Gates
• 8 minute presentations
• 6 min for research, 2 min for questions
• Start with an overview
• 1 sentence statement of your research result
• 1 slide / 4 sentences of what you did and why
• Rest of time on details. Assume audience is
  familiar with HCI issues focus on your work

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Final Project Participation

• Be sure to fill out the survey accompanying
  Sharons paper reader!
• Do others want to ask the class to participate in
  their research?

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Direct Manipulation vs. Agents!

• Public Debates at CHI 97, IUI 97
• Ben Shneiderman for Direct Manipulation
• Pattie Maes for Interface Agents

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Ben Shneiderman

• I think we would do best to focus on the
  remarkable human capabilities in the visual
  domain, which I think are largely under-utilized
  by the current designs with 40 icons in 2-3
  windows. I think we should have two or three
  orders of magnitude more 4,000 or more items on
  the screen in an orderly way that enables people
  to see all of the possibilities and navigate
  among them.

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The Direct Manipulation Ideology

• Display as much information as possible
• Predictable
• Rapid, reversable interactions
• User initiates all actions

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The goal high information density
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Command Line Low density and indirect
manipulation
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guis can provide improved density and more direct
manipulation
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but still have a ways to go
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Pattie Maes

• Why do we need software agents? Take a look at
  the World Wide Web, for example. You couldnt
  possibly try to visualize the World Wide Web in
  any way because it is completely unstructured and
  because it has been built by so many different
  people and is continuously changing. I believe
  that the dominant metaphor that we have today is
  a mismatch for the computer environment we are
  dealing with tomorrow.

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The Intelligent Interfaces Ideology

• Agents know habits, preferences, interests
• Tasks can be delegated to software agents
• Mixed-initiative computer can be proactive
• prompt-based telephone interfaces are an example
  of complete computer initiative

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Some Successes

• Spam Filtering
• Collaborative Filtering
• Toyota Prius power train and braking

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Failures?
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DM v. Collaborative Filtering
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Pros and Cons?

• Predictability and Intelligibility
• Personalization and Adaptation
• Reactive vs. Proactive
• Scalability, Accuracy, Anthropomorphism

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Adaptive Interfaces

• Build model of user and/or context (device)
• Preferences
• Abilities
• Build optimization / selection criteria
• Adapt interface design or take action
• Perform optimization/search with model criteria
• Change layout, highlighting, exposed features
• Recommend selected options / items
• (Sometimes) make decisions on when to act

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Menus Does Adaptation Help?
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Collaborative Filtering

• Recommend based on similarity to others

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Collaborative Filtering

• Recommend based on similarity to others
• How to evaluate?
• Accuracy measures (mismatch with user ratings)
• Novelty, non-obviousness
• Coverage how much should be recommended?
• Algorithms vary based on data ( users vs.
  items)
• Inherent variability peoples ratings may not be
  consistent over multiple samples

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SuppleGajos et al
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User Preference Elicitation
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Model Motor Abilities
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(No Transcript)
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Evaluation
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Personal Universal Controller Nichols et al
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Personal Universal Controller

• How to deal with multiplicity of devices?
• How to control ubicomp environments?
• Specification language
• Specify complete functionality
• of appliances
• Use specifications to generate
• interfaces for mobile devices

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Ensuring Interface Consistency
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Trade-offs for Generated UIs

• Personalized to preferences / abilities
• Cross-device functionality
• Poor aesthetics
• Only valuable when human designer absent?

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CS547 Tomorrow

• Designing Online Communities from Theory
• Robert Kraut, CMU HCI Institute
• 1230-2pm, Gates B1