Context, Zones, and Usability Marti Hearst UC Berkeley Inktomi Seminar April 28, 2000.
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Transcript of Context, Zones, and Usability Marti Hearst UC Berkeley Inktomi Seminar April 28, 2000.
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Context, Zones, and Usability
Marti Hearst
UC Berkeley
Inktomi Seminar
April 28, 2000
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My Background
Databases
Natural Language Processing
Human-Computer Interaction
User Interfaces for Text Search
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TileBars
Scatter/Gather
DynaCat
Cat-a-Cone
Search Interfaces:Past Projects
Common Themes:
Search Result Context
Integrating Browsing & Search
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Outline
What are context zones? The importance of the task UI / HCI ideas & projects
Workspaces Information previews Alternative UIs
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WWWWWW
Context Zones
IndustryIndustry
IntranetIntranet
DesktopDesktop
Cascading priority based on locality of information
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WWWWWW
Context Zones
IndustryIndustry
IntranetIntranet
DesktopDesktop
Specific slice through the data: analyst vs salesperson, or legal vs. medical
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WWWWWW
Context Zones
IndustryIndustry
IntranetIntranet
DesktopDesktop
Slice again based on task, e.g., research vs reporting
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Why do this?
General search is too broad Allows for customization of search space
Eliminates irrelevant information in advance Reduces ambiguity of query word usage Uses the user’s background
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Slicing by Topic Only
Example: FindLaw A vertical slice through legal text
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Slicing by Topic Only
Is subject-specific search enough? Should better support different legal tasks
Find prior art for patent infringement case Find weaknesses in the application of
intellectual property law in the 6th circuit court of appeals
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Combining Collections
News Business News Legal News Science News
Science News Reports Patents
Legal News Patents Law Schools
What will users be using these for?
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The Importance of the Task
Results from HCI suggest the importance of taking the task into account.
Proving non-infringement
(vs searching patent databases) Finding the denial-of-service hacker
(vs browsing newsgroups) Anticipating the competition’s marketing strategies
(vs getting all satellite news)
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The Importance of the Task
Example: Does download time matter? In one study, Spool found: (56kbit modem)
Amazon: 36 sec/pg (avg) About.com: 8 sec/pg (avg)
Users rated the sites: Fastest: Amazon Slowest: About.com
Why?
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The Importance of the Task
Perceived speed Strong correlation between perceived speed
and whether the users felt they completed their task
Strong correlation between perceived speed and whether the users felt they always knew what to do next (scent).
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How to incorporate the task?
Workspaces Relevant information previews Task-sensitive question-answering
Simplicity / Flexibility Tradeoff
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Workspace
The grouping together of sets of windows known to be functionally related to some activity or goal. (Bannon et al. 83)
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Early Workspaces Xerox PARC
Rooms (Hendersen & Card 86)
Sun/HP X-windows task grouping
Elastic Windows (Kandogan & Shneiderman 97)
Task:General work context
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Workspaces
Restrict combinatons: Particular task type(s) Particular domain Relevant information collections Relevant operation types
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The DLITE Workspace
By Steve Cousins (Stanford PhD, now at PARC)
Task-oriented workspace Specialized tools, collections, query forms
A distributed information system Show network, remote server status Concurrently shareable across sites
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DLITE (Cousins 97)
Task: Technical research
Sources: Bibliographic
collection, WWW Operators:
Summarize documents
Translate documents Extract references
Query form: Title, Author, Subject
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Workspaces restrict the tools …
… but there can still be too many items returned as the result of a search.
Need to focus on the task in more detail.
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Information Previews
Give users a hint of what happens next Help users see and return to what
happened previously Reduces mental work
Recognition over recall Memory aid
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Metadata-based Customization
Time/Date Topic RoleGeoRegion
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Task-Specific Preview CombinationsA Simple Example
Yahoo restaurant guide combines: Region Topic (restaurants) + Attributes (cuisine) Related Information
Other attributes (cuisines) Other topics related in place and time (movies)
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Green: restaurants & attributes
Red: related in place & time
Yellow: region
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Combining Information Types
Region State
City
A & E Film Theatre Music Restaurants
California Eclectic Indian French Assumed task: looking for
evening entertainment
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Other Possible Combinations
Region + A&E City + Restaurant + Movies City + Weather City + Education: Schools Restaurants + Schools …
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Bookstore preview combinations topic + related topics topic + publications by same author topic + books of same type but related topic
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Combining Information Types:Information “Appliances” Palm Pilot
Sweet spot Suite spot Smart Cars
Driving directions, traffic conditions Nearby restaurants Car status, gas stations, nearby repair shops Indy 500 results?
Smart Coffee Maker When to brew, warm up, turn off Coffee futures in Brazil?
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The Importance of Informative Previews
Jared Spool’s studies (www.uie.com) More clicks are ok if
The “scent” of the target does not weaken If users feel they are going towards, rather
than away, from their target.
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The Importance of Informative Previews
How to indicate “scent”? Information organization reflects tasks Longer, more descriptive links Show category subtopic information Breadth vs. depth tradeoffs
CNN categores (more scrolling) vs. Yahoo’s (more clicking) Menu studies Larson & Czerwinski study Intermediate breadth vs.
depth generally best
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Showing Where You’re Going
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Simplicity / Flexibility Tradeoff Wizard Hyperlinks (categories) Search results + related docs / words Search results + related metadata
increasingflexibility
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Spreadsheets
Highly flexible Several operators Many orders to use & combine them in
What gets used? (Nardi 93) Most people learn a very limited subset of
operations, use these in stereotyped ways Most groups depend on local experts
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Problem with Previews
Problems with Previews Hand edited, predefined Not personalized Not dynamic
Should users edit these themselves?
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Personalizing CombinationsMobile People Architecture (M. Baker)
Route information to the right device, with the right resolution, at the right time
Examples: Stop phones from ringing in empty offices, or at home during dinner
Convert voice to email or video to voice, etc. Uses condition-action rules to combine:
Sender Recipient Sender Media types Receiver Media types Time of day Words in title Words in body of message
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Personalized Condition-Action Rules
(From Roussopoulos et al, USITS 99)
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Personalized Condition-Action Rules
Problems: Complex Brittle
Who puts them together? The user? A human editor? The system?
What are the right criteria? Easier for common or stereotypical scenarios Difficult for information-intense processes
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Dynamic Previews
Flamenco project Preview and postview information determined
dynamically and (semi) automatically, based on current task
Medical example Allow user to select metadata in any order At each step, show different types of relevant metadata,
based on prior steps and personal history, along with # of documents
Could not precompute all possible combinations Previews restricted to only those types that might be helpful
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Medical preview combinations
Disease
Procedure
Side Effects
Products
Hospitals Region
differentmetadatatypes
This patient’s allergies
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Question Answering
Ask Jeeves does this by hand One answer per questions Question/answer pairs don’t generalize well
Alternative: Use the task to Restrict the kinds of questions being asked Restrict the kinds of answers that are shown
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DynaCat: An Approach to Task-Specific Q/A By Wanda Pratt (Stanford PhD, now at UC Irvine)
Domain: Medicine Collection: Medical research articles Task: Layperson wanting detailed
information on a particular aspect of disease Technique:
Question types Answers organization based on question type
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DynaCat Strategy (Pratt, Hearst, & Fagan 99)
Identify generally useful question types What is the prognosis for disease D? What are the side-effects of drug P?
Identify generally useful categories for the answers
Behavior Chemicals & Drugs
Use these categories only to organize retrieved documents.
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DynaCat Screenshot
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DynaCat Study Design
Three queries 24 cancer patients Compared three interfaces
ranked list, clusters, categories
Results Participants strongly preferred categories Participants found more answers using categories Participants took same amount of time with all three
interfaces Another study also favors categories over lists (Chen
and Dumais, CHI 2000)
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Task-sensitive question answering
This approach is restricted, but at the same time somewhat general Applicable to thousands of queries Continues to work even if underlying datasets
change
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Specialized UIs
The type of information should also structure the interface Chat rooms Legal cases Software documentation
How should the type of data influence the type of UI?
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Conversation Maps, Sack 99
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Chat histories, Viegas & Donath 99
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An Additional Problem
Assuming we have many task-specific combinations of collections and UIs …
… how to get the users to the right ones at the right time?
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Current Projects
CHA-CHA, FLAMENCO: Search Interfaces
LINDI: Text Data Mining
TANGO: Automated Web Site Usability
Assessment
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Summary
Customizable zone architecture: great idea! Task-centric approaches
Workspaces Showing next choices / previews Task-sensitive question answering Special search UIs
Issues How to build these? Given lots of task-specific UIs, how to find the right one?