1. Adaptive Interfaces
Scott Klemmer · 09 November 2006

2. Midquarter Evaluation
8 responses; thanks!
Responders generally enthusiastic about readings and format; one dissenter: “basic literature should not be reviewed”
Three areas for improvement
“not enough time to do all the readings, write the critiques and get enough sleep to go to class and participate”
“Some way to know how we're doing in the class.” (especially with projects)
“I think the student presentations should be more focused on interaction than lecturing”
Overall: Excellent / Very Good / Good / Very Good / Poor / Fair / Very Good / Excellent
Change time readings are due?
Some way

3. The Direct Manipulation Ideology
Display as much information as possible
Predictable
Rapid, reversable interactions
User initiates all actions

4. The goal: high information density

5. Command Line: Low density and indirect manipulation

6. guis have improved density and more direct manipulation…

7. …but still have a ways to go

8. Ben Shneiderman on design methods
“30 years of planning work in AI is essentially down the tubes because of lack of attention to the user interface. The designers deliver a system and the first thing that the users say is, ‘This is great but what we really want to do is change these parameters.’ The designers say, ‘Well, you know, we didn’t put that in the interface.’ They just haven’t thought adequately about the interface, nor done testing early enough.”

9. The Intelligent Interfaces Ideology
Agents know habits, preferences, interests
Mixed initiative: computer is sometimes proactive
prompt-based telephone interfaces are an example of complete computer initiative

10. Some recent successes
Spam Filtering
Toyota Prius braking system

11. How Spam Filtering Works
Uses a Bayesian network
Begin with a set of ham (good) and spam messages
Look at tokens ( addresses, words) and their relative frequencies in ham and spam
e.g., “mortgage” occurs in 400 of 3,000 spam mails and 5 out of 300 legitimate s. Its spam probability would be ([400/3000] divided by [5/ /3000]).

12. Understanding Intelligent UIs
q “Why was this message classified as spam?”

15. Collaborative Filtering
aka recommender systems
Introduced in 1992, roughly simultaneously by…
David Goldberg, Xerox parc ( )
Joe Konstan, Berkeley ->umn (NetNews)
…and explored soon after by many, including
Pattie Maes, mit media lab (music)

16. Traditional DM v. Collaborative Filtering

17. How do they work? An Example Algorithm
Yezdezard Lashkari, Feature Guided Automated Collaborative Filtering, Masters Thesis, MIT Media Laboratory, 1995.
Webhound
Firefly

18. Webhound, Lashkari, 1995

19. Webhound, Lashkari, 1995

20. Webhound, Lashkari, 1995

21. Webhound, Lashkari, 1995

22. Attentional Interfaces
Chris Schmandt (MIT Media Lab)
James Fogarty & Scott Hudson (CMU)
Eric Horvitz (MSR)

23. Everywhere Messaging
C. Schmandt, N. Marmasse, S. Marti, N. Sawhney, S. Wheeler, IBM Systems Journal, 2000
Several systems
Clues: Finds time-critical s
Active Messenger: Delivers these to one of many devices
Nomadic Radio: Wearable audio
comMotion: Location-aware

24. Clues

25. Active Messenger

26. Nomadic Radio

27. comMotion

28. Next Time… Capture & Access
The Audio Notebook, Lisa Stifelman, Barry Arons, Chris Schmandt
Lessons Learned from eClass: Assessing Automated Capture and Access in the Classroom, Jason A. Brotherton and Gregory D. Abowd