Pat Langley

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Pat Langley Institute for the Study of
Learning and Expertise Palo Alto,
California and Center for the Study of Language
and Information Stanford University, Stanford,
California http//cll.stanford.edu/langley langle
y_at_csli.stanford.edu
Adaptive User Interfaces for Personalized Services
Thanks to D. Billsus, M. Chen, C.-N. Fiechter, M.
Gervasio, M. Goker, W. Iba, S. Rogers, C.
Thompson, and J. Yoo.
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The Need for Personalized Assistance

• We now have more information and choices
  available than ever before, and we need help to
  handle them effectively.

This has led to recommendation systems, which
help users locate and select relevant items.
But often we want personalized assistance that
takes into account our individual preferences.
However, such personalized response requires a
user model or profile that is constructed in some
manner.
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Approaches to User Modeling
Individual Profiles
Stereotypical Profiles
Hand-crafted Profiles
Hand-crafted Stereotypes
Manual Construction
Adaptive User Interfaces
Data-Mining Methods
Automated Construction
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Definition of an Adaptive User Interface
a software artifact
by acquiring a user model
that reduces user effort
based on past user interaction
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Definition of a Machine Learning System
a software artifact
by acquiring knowledge
that improves task performance
based on partial task experience
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Applications of Adaptive User Interfaces
Web browsing
in-car navigation
news filtering
interactive scheduling
book selection
Email filing
stock tracking
TV selection
apartment selection
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Inferring Individual User Profiles
Tasks that require a user decision
Mapping from task features onto user decisions
Find
A description for each task
Traces of the users decisions
Our work focuses on content-based approaches to
adaptive user interfaces, rather than on
collaborative approaches.
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The Task of Route Selection
One decision-making task that confronts drivers
can be stated as

• Given The drivers current location C
• Given The destination D that the driver
  desires
• Given Knowledge about available roads (e.g., a
  digital map)
• Find One or more desirable routes from C to D.

Navigation aides already exist in both vehicles
and on the World Wide Web.
However, they do not give personalized navigation
advice to individual drivers.
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The Adaptive Route Advisor
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Generating Training Cases
The Adaptive Route Advisor represents the driver
model as a weighted linear combination of route
features.
Time
Distance
? w0
?
? w1
Cost
? w2
Intersections
? w3
Turns
Training cases x0, . . . , xn is better than
y0, . . . , yn.
The system uses each training pair as constraints
on the weights found during the modeling process.
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Experimental Results on Route Advice
Personalized user models produce better results
than generalized models, even when the latter are
based on more data.
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The Task of News Reading
Another service that would benefit drivers can be
stated as

• Given Topics and events that interest the
  driver
• Given Recent news stories available on the Web
• Given Knowledge about stories the driver has
  heard
• Find Stories to read the driver during the
  current trip.

Many online news services are available on the
World Wide Web, but few offer personalized
selection.
Moreover, they are ill suited for use in the
driving environment, where visual attention is a
limited resource.
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News Dude (Billsus Pazzani, 1999)
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Adaptive News Readers in the Marketplace
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The Task of Destination Selection
A third type of service that would assist drivers
can be stated as

• Given Some goal the driver wants to achieve
  (e.g., eat lunch)
• Given Constraints on destinations that achieve
  this goal
• Given Knowledge about available nearby
  destinations
• Find One or more destinations that satisfy
  the constraints.

Many recommendation systems are available on the
World Wide Web, including ones that suggest
restaurants.
However, they are not designed for use by
drivers, who must keep their eyes on the road.
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The Adaptive Place Advisor
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Adaptive Place Advisor (Before Adaptation)
Inquirer Where do you think I should eat
tonight? Advisor What type of food would you
like? Inquirer What types are there? Advisor
You can say things like Chinese, Indian, and
Mediterranean. Inquirer Oh, maybe a French
place with free parking. Advisor What quality
rating would you like? Inquirer I don't care,
as long as it's cheap. Advisor Im sorry, I
dont know any restaurants like that.
Would you like to search for any price?
Inquirer No, I think I'd like Chinese instead.
Advisor In what city would you like to eat?
Inquirer How about Berkeley? Advisor How does
the Long Life Vegi House on 2129 University
Avenue sound? Inquirer Sure, that sounds fine.
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Adaptive Place Advisor (After Adaptation)
Inquirer Where do you think I should eat
tonight? Advisor What type of food would you
like? Inquirer Cheap Chinese. Advisor What
city do you prefer? Inquirer How about Palo
Alto? Advisor How does Jing Jing Szechuan
Gourmet on 443 Emerson sound?
Inquirer Sure, that sounds fine.
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Speech Acts Per Conversation with Adaptive Place
Advisor

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INCA An Adaptive Scheduler
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BackFlip Personalized Bookmarking
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Personalized Music Delivery
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A Personalized Travel Agent
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An Adaptive Apartment Finder
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An Adaptive Stock Tracker
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Alternative Presentation Styles
Sequential
Tweaked Set
Ranked List
Classification
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A Flexible Framework for Adaptive Interfaces
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Challenges in Developing an Adaptive Interface
Formulating the Problem
Engineering the Representation
Collecting User Traces
Modeling Process
Utilizing Model Effectively
Gaining User Acceptance
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Contributions of the Research
Our research program on adaptive user interfaces
has produced

• a variety of artifacts that learn user
  preferences unobtrusively
• evidence that this approach to user modeling is a
  general one
• experimental support for the effectiveness of
  these systems
• an analysis of presentation styles possible for
  such systems
• a flexible framework for constructing them
  efficiently and
• clarification of issues that arise in their
  effective design.

Although some issues remain, we understand
adaptive interfaces well enough to apply them in
practical services.
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Directions for Future Research
Despite clear progress on adaptive user
interfaces, we must still

• design methods to combine stereotypes and
  individual profiles
• create approaches that transfer user profiles
  across domains
• apply these techniques to an ever wider range of
  problems
• utilize new sensors to collect data even less
  obtrusively and
• develop complete physical environments that adapt
  to users.

Together, these advances will lead us toward a
society in which personalized computational aides
are a regular part of our lives.
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Dialogue Operators for Adaptive Place Advisor
System Operators Ask-Constrain Asks a question to
obtain a value for an attribute Ask-Relax Asks a
question to remove a value of an
attribute Suggest-Values Suggests a small set of
possible values for an attribute Suggest-Attribute
s Suggests a small set of unconstrained
attributes Recommend-Item Recommends an item
that satisfies the current constraints Clarify Ask
s a clarifying question if uncertain about latest
user operator User Operators Provide-Constrain Pr
ovides a value for an attribute Reject-Constrain R
ejects the proposed attribute Accept-Relax Accepts
the removal of an attribute value Reject-Relax Re
jects the removal of an attribute
value Accept-Item Accepts the proposed item
Reject-Item Rejects the proposed
item Query-Attributes Asks system for information
about possible attributes Query-Values Asks
system for information about possible attribute
values Start-Over Asks the system to
re-initialize the search Quit Asks the system to
abort the search