Organizing Search Results - PowerPoint PPT Presentation

Organizing Search Results

Susan Dumais

Microsoft Research

• Algorithms and interfaces that improve the effectiveness of search
• Beyond ranked lists
• Main goal to support search
• Also information analysis and discovery
• Example applications
• SWISH, results classification
• GridViz, results summarization
• SIS, personal landmarks for context

• Collaborators
• Edward Cutrell, Hao Chen (Berkeley)
• Key Themes
• Going beyond long lists of results
• Classification algorithms
• UI techniques
• More about it
• http://research.microsoft.com /~sdumais

Organizing Search Results

List Organization

SWISH Category Organization

=> Shopping

=> Automotive

=> Computers

=> Automotive

Query: “jaguar”

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

• LookSmart Directory Structure
• ~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

• Combines the advantages of
• 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
• Text classification models
• UI for integrating search results and structure
• Context (category structure) plus focus (search results)

...

web

search

results

local

search

results

Train

(offline)

Classify

(online)

manually

classified

web

pages

SVM

model

• Support Vector Machine (SVM)
• Accurate and efficient for text classification (Dumais et al., Joachims)
• Model = weighted vector of words
• “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

User Interface Experiments

List Organization

Category Organization

No Cat Names

+ Cat Names

Hover

Inline

Browse

Hover

Inline

Group Interface

List Interface

Easy queries are faster(p<0.01)

• Group faster than List(p<0.01)
• Benefit is larger for hard queries(p<0.06)

HARD

HARD

EASY

EASY

Group

List

Effect of Query Difficulty

• Text Classification
• Learn accurate category models
• Classify new web pages on-the-fly
• Organize search results
• User Interface
• Tightly couple search results with category structure
• User manipulation of presentation of category structure

• Collaborators
• George Robertson, Edward Cutrell, Jeremy Goecks (Georgia Tech)
• Key Themes
• Abstract beyond individual results
• Highly interactive interface to support understanding of trends and relationships
• More about it
• http://research.microsoft.com/~sdumais

• Summarize the results of a search
• Grid-based design
• Axes represent topic, time, people
• Cells encode frequency, recency
• 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?

List View

GridViz

User Interface Experiments

• Abstracting beyond individual results
• Highly interactive interface
• Grid-based design
• Axes represent people, topic, time
• Cells encode frequency, recency
• Preliminary but promising

• Collaborators
• Edward Cutrell, Raman Sarin, JJ Cadiz, Gavin Jancke, Daniel Robbins, Merrie Ringel (Stanford)
• Key Themes
• Your content
• Information re-use
• Integration across sources
• More about it
• … internal for now

• Many locations, interfaces for finding things (e.g., web, mail, local files, help, history, intranet)

• Often slow

• Unified index of stuff you’ve seen
• 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
• Client side indexing and storage
• Built using MS Search components

• 800+ internal users
• 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

• Use of other search tools
• Non-SIS search for web, email, and files decreases
• Importance of people
• 25% of the queries involve people’s names
• Importance of time
• Date by far the most popular sort field, followed by rank, author, title
• Even when rank is the default

• Importance of time
• Timeline interface
• Contextualize results using important landmarks as pointers into human memory
• General: holidays, world events
• Personal: important photos, appointments

• 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
• 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

• Algorithms and interfaces to improve search
• Use structure and context
• Examples and key themes
• SWISH … grouping
• GridViz … abstraction
• SIS … personal content and landmarks
• Also
• Important attributes: People, topics, time
• Interaction
• Evaluation
• More information
• http://research.microsoft.com/~sdumais
• sdumais@microsoft.com
• Christopher Lee of (SIG)IR …
• http://www.cdvp.dcu.ie/SIGIR/index.html