Transcript Shiffrin Symposium - Indiana University

Organizing Search
Results
Susan Dumais
Microsoft Research
Sackler – May 11, 2003
Organizing Search Results

Algorithms and interfaces that improve the
effectiveness of search
Beyond ranked lists
 Main goal to support search
 Also information analysis and discovery
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Example applications
SWISH, results classification
 GridViz, results summarization
 SIS, personal landmarks for context
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Searching with Information
Structured Hierarchically (SWISH)
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Collaborators
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Edward Cutrell, Hao Chen (Berkeley)
Key Themes
Going beyond long lists of results
 Classification algorithms
 UI techniques
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More about it

http://research.microsoft.com /~sdumais
Sackler – May 11, 2003
Organizing Search Results
Query: “jaguar”
List Organization
=> Shopping
=> Automotive
=> Computers
=> Automotive
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SWISH Category Organization
Web Directory

LookSmart Directory Structure
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~400k pages; 17k categories; 7 levels
13 top-level categories; 150 second-level categories
Top-level Categories
Automotive
Business & Finance
Computers & Internet
Entertainment & Media
Health & Fitness
Hobbies & Interests
Home & Family
People & Chat
Reference & Education
Shopping & Services
Society & Politics
Sports & Recreation
Travel & Vacations
Sackler – May 11, 2003
Buy or Sell a Car
Chat
Finance & Insurance
Magazines & Books
Maintenance & Repair
Makes, Models & Clubs
Motorcycles
New Car Showrooms
Off-Road, 4X4 & RVs
Other Auto Interests
Shows & Museums
Trucks & Tractors
Vintage & Classic
SWISH System
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Combines the advantages of
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Directories - Manually crafted structure but small
<~3 million pages>
Search engines - Broad coverage but limited
metadata <~3 billion pages>
Project search engine results to category structure
Two main components
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Text classification models
UI for integrating search results and structure

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Context (category structure) plus focus (search results)
SWISH Architecture
Train
(offline)
manually
classified
web
pages
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Classify
(online)
SVM
model
web
search
results
local
search
results
...
Learning & Classification

Support Vector Machine (SVM)
Accurate and efficient for text classification
(Dumais et al., Joachims)
 Model = weighted vector of words
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“Automobile” = motorcycle, vehicle, parts, automobile, harley,
car, auto, honda, porsche …
“Computers & Internet” = rfc, software, provider, windows,
user, users, pc, hosting, os, downloads ...
Hierarchical models for LS directory
1 model for top level; N models for second
 Very useful in conjunction w/ user interaction

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User Interface Experiments
List Organization
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Category Organization
No Cat Browse Hover Inline + Cat
Hover Inline Names
Names
Group Interface
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List Interface
Effect of Query Difficulty
Easy queries
are faster (p<0.01)
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H
A
R
D
E
A
S
Y
Group
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H
A
R
D
E
A
S
Y
Group faster
than List (p<0.01)
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Benefit is
larger for hard
queries (p<0.06)
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List
SWISH: Summary and
Design Implications
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Text Classification
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Learn accurate category
models
Classify new web pages onthe-fly
Organize search results
User Interface
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Tightly couple search results
with category structure
User manipulation of
presentation of category
structure
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GridViz
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Collaborators
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George Robertson, Edward Cutrell, Jeremy
Goecks (Georgia Tech)
Key Themes
Abstract beyond individual results
 Highly interactive interface to support
understanding of trends and relationships
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More about it

http://research.microsoft.com/~sdumais
Sackler – May 11, 2003
GridViz
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Summarize the results of a search
Grid-based design
Axes represent topic, time, people
 Cells encode frequency, recency
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Supports activities like:
What newsgroups are active (on topic x)?
 What people are active, authoritative (on topic x)?
 When did I last interact w/ people?
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GridViz Demo
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User Interface Experiments
List View
GridViz
40
35
30
25
20
15
10
5
0
G rid V iz
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L is t -v ie w
GridViz Summary
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Abstracting beyond individual results
Highly interactive interface
Grid-based design
Axes represent people, topic, time
 Cells encode frequency, recency
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Preliminary but promising
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Stuff I’ve Seen (SIS)

Collaborators
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Edward Cutrell, Raman Sarin, JJ Cadiz,
Gavin Jancke, Daniel Robbins, Merrie Ringel
(Stanford)
Key Themes
Your content
 Information re-use
 Integration across sources

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More about it

… internal for now
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Search Today …
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Many locations, interfaces for
finding things (e.g., web, mail, local
files, help, history, intranet)
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Often slow
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Search with SIS
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Unified index of stuff you’ve seen
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Unify access to information regardless of source –
mail, archives, calendar, files, web pages, etc.
Full-text index of content plus metadata attributes
(e.g., creation time, author, title, size)
Automatic and immediate update of index
Rich UI possibilities, since it’s your content
Architecture
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Client side indexing and storage
Built using MS Search components
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SIS Demo
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SIS Alpha Observations
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800+ internal users
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Usage logs (incl different interfaces), survey data
File types opened
76% Email
 14% Web pages
 10% Files

Age of items accessed
7% today
 22% within the last week
 46% within the last month
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120
100
Frequency

Item Access Distribution
80
60
40
20
0
0
500
1000
1500
2000
Days Since Item First Seen
2500
SIS Alpha Observations
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Use of other search tools
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Importance of people
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Non-SIS search for web,
email, and files decreases
25% of the queries involve
people’s names
Importance of time

Date by far the most
popular sort field, followed
by rank, author, title
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Even when rank is the default
6
5
Pre-usage
Post-usage
4
3
2
1
0
Files
Email
Web Pages
SIS UI Innovations
Timeline w/ Landmarks
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Importance of time
 Timeline

interface
Contextualize results using
important landmarks as pointers
into human memory
General: holidays, world events
 Personal: important photos, appointments
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Milestones in Time Demo
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Milestones in Timeline
30
Search Time (s)
25
20
15
10
5
0
Landmarks + Dates
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Dates Only
SIS Summary

Unified index of stuff you’ve seen
Fast access to full-text and metadata, from
heterogeneous sources
 Automatic and immediate update of index
 Rich UI possibilities
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Next steps
Better support for tagging -> “flatland”
 Implicit queries for finding related info, and
identifying “Stuff I Should See”
 Integration with richer activity-based info, Eve
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Sackler – May 11, 2003
Organizinging Search Results

Algorithms and interfaces to improve search


Examples and key themes

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Important attributes: People, topics, time
Interaction
Evaluation
More information

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SWISH … grouping
GridViz … abstraction
SIS … personal content and landmarks
Also

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Use structure and context
http://research.microsoft.com/~sdumais
[email protected]
Christopher Lee of (SIG)IR …

http://www.cdvp.dcu.ie/SIGIR/index.html
Sackler – May 11, 2003