Organizing Search Results

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Organizing Search Results

• Susan Dumais
• Microsoft Research

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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
• Example applications
• SWISH, results classification
• GridViz, results summarization
• SIS, personal landmarks for context

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Searching with Information Structured
Hierarchically (SWISH)

• 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

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Organizing Search Results
Query jaguar
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Web Directory

• 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
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SWISH System

• Combines the advantages of
• Directories - Manually crafted structure but
  small
• Search engines - Broad coverage but limited
  metadata
• 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)

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SWISH Architecture
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Learning Classification

• 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

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User Interface Experiments
List Organization
Category Organization
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Effect of Query Difficulty
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SWISH Summary and Design Implications

• 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

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Organizing Search Results, other examples
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GridViz

• 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

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GridViz

• 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?

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GridViz Demo
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User Interface Experiments
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GridViz Summary

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

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Stuff Ive Seen (SIS)

• 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

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Search Today

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

• Often slow

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Search with SIS

• Unified index of stuff youve 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 its your content
• Architecture
• Client side indexing and storage
• Built using MS Search components

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SIS Demo
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SIS Alpha Observations

• 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

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SIS Alpha Observations

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

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SIS UI InnovationsTimeline w/ Landmarks

• 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
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SIS Summary

• Unified index of stuff youve 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

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Organizinging Search Results

• 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_at_microsoft.com
• Christopher Lee of (SIG)IR
• http//www.cdvp.dcu.ie/SIGIR/index.html