i247: Information Visualization and Presentation Marti Hearst

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i247 Information Visualization and
PresentationMarti Hearst
April 7, 2008    
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Search and Text Visualization

• Nominal data is hard to visualize
• Goals of search vs. text analysis
• What works well for each?

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When people are searching
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Search Viz Meta-Analysis

• Chen Yu 2000
• Individual cognitive differences among
  participants had the largest effect, especially
  on accuracy, and to some degree on efficiency,
• Holding cognitive abilities constant,
  participants performed better with simpler
  visual-spatial interfaces than with complex ones,
  and
• The combined effect of visualization in the
  studies was not statistically significant.

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Query Term Visualization

• Fancy Boolean vizs are better than boolean
  command lines but still not used
• Term suggestions arranged as a cloud can be
  liked, but unlikely to be better than a simple
  list of term suggestions

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Search Results Visualization

• Show location of term hits within retrieved
  documents
• TileBars was first
• There have been many variations since

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TileBars Viewing Retrieval Results

• Goal minimize time/effort for deciding which
  documents to examine in detail
• Idea show the roles of the query terms in the
  retrieved documents, making use of document
  structure

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TileBars (Hearst 94)
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Exploiting Visual Properties

• Variation in gray scale saturation imposes a
  universal, perceptual order (Bertin et al. 83)
• Varying shades of gray show varying quantities
  better than color (Tufte 83)
• Differences in shading should align with the
  values being presented (Kosslyn et al. 83)

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Other Variations

• HotMaps, Hoeber Yang, Pie Charts, Anderson et
  al.

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A Comparative Study

• Reiterer et al., SIGIR 2000
• Well-done study
• They werent the creators of the vizs tested
• 40 participants, varied tasks
• Compared
• Plain html web page
• Sortable search results (in a table view)
• Tilebars-like view
• Bar charts view
• Scatterplot view
• Results
• People werent any better with vizs than with
  standard web view. Significantly worse with bar
  charts
• Subjective results Sortable Table, then
  Tilebars, then simple web-based view
• People hated bar charts and scatter plots

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Cluster-based Grouping

• Document Self-similarity
• (Polythetic)

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Scatter/Gather Clustering

• Developed at PARC in the late 80s/early 90s
• Top-down approach
• Start with k seeds (documents) to represent k
  clusters
• Each document assigned to the cluster with the
  most similar seeds
• To choose the seeds
• Cluster in a bottom-up manner
• Hierarchical agglomerative clustering
• Can recluster a cluster to produce a hierarchy of
  clusters

Pedersen, Cutting, Karger, Tukey, Scatter/Gather
A Cluster-based Approach to Browsing Large
Document Collections, SIGIR 1992
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The Scatter/Gather Interface
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Two Queries Two Clusterings
AUTO, CAR, ELECTRIC
AUTO, CAR, SAFETY
8 control drive accident 25 battery
california technology 48 import j. rate
honda toyota 16 export international unit
japan 3 service employee automatic
6 control inventory integrate 10
investigation washington 12 study fuel death
bag air 61 sale domestic truck import 11
japan export defect unite
The main differences are the clusters that are
central to the query
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Scatter/Gather Evaluations

• Can be slower to find answers than linear search!
• Difficult to understand the clusters.
• There is no consistence in results.
• However, the clusters do group relevant documents
  together.
• Participants noted that useful for eliminating
  irrelevant groups.

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Visualizing Clustering Results

• Use clustering to map the entire huge
  multidimensional document space into a huge
  number of small clusters.
• User dimension reduction and then project these
  onto a 2D/3D graphical representation

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Clustering Visualizationsimage from Wise et al
95
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Clustering Visualizations(image from Wise et al
95)
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Kohonen Feature Maps(Lin 92, Chen et al. 97)
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Are visual clusters useful?

• Four Clustering Visualization Usability Studies
• Conclusions
• Huge 2D maps may be inappropriate focus for
  information retrieval
• cannot see what the documents are about
• space is difficult to browse for IR purposes
• (tough to visualize abstract concepts)
• Perhaps more suited for pattern discovery and
  gist-like overviews.

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Clustering for Search Study 1

• This study compared
• a system with 2D graphical clusters
• a system with 3D graphical clusters
• a system that shows textual clusters
• Novice users
• Only textual clusters were helpful (and they were
  difficult to use well)

Kleiboemer, Lazear, and Pedersen. Tailoring a
retrieval system for naive users. SDAIR96
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Clustering Study 2 Kohonen Feature Maps, Chen
et al.

• Comparison Kohonen Map and Yahoo
• Task
• Window shop for interesting home page
• Repeat with other interface
• Results
• Starting with map could repeat in Yahoo (8/11)
• Starting with Yahoo unable to repeat in map (2/14)

Chen, Houston, Sewell, Schatz, Internet Browsing
and Searching User Evaluations of Category Map
and Concept Space Techniques. JASIS 49(7)
582-603 (1998)
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Kohonen Feature Maps(Lin 92, Chen et al. 97)
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Study 2 (cont.), Chen et al.

• Participants liked
• Correspondence of region size to documents
• Overview (but also wanted zoom)
• Ease of jumping from one topic to another
• Multiple routes to topics
• Use of category and subcategory labels

Chen, Houston, Sewell, Schatz, Internet Browsing
and Searching User Evaluations of Category Map
and Concept Space Techniques. JASIS 49(7)
582-603 (1998)
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Study 2 (cont.), Chen et al.

• Participants wanted
• hierarchical organization
• other ordering of concepts (alphabetical)
• integration of browsing and search
• correspondence of color to meaning
• more meaningful labels
• labels at same level of abstraction
• fit more labels in the given space
• combined keyword and category search
• multiple category assignment (sportsentertain)
• (These can all be addressed with faceted
  categories)

Chen, Houston, Sewell, Schatz, Internet Browsing
and Searching User Evaluations of Category Map
and Concept Space Techniques. JASIS 49(7)
582-603 (1998)
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Clustering Study 3 Sebrechts et al.

• Each rectangle is a cluster. Larger clusters
  closer to the pole. Similar clusters near one
  another. Opening a cluster causes a projection
  that shows the titles.

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Study 3, Sebrechts et al.

• This study compared
• 3D graphical clusters
• 2D graphical clusters
• textual clusters
• 15 participants, between-subject design
• Tasks
• Locate a particular document
• Locate and mark a particular document
• Locate a previously marked document
• Locate all clusters that discuss some topic
• List more frequently represented topics

Visualization of search results a comparative
evaluation of text, 2D, and 3D interfaces
Sebrechts, Cugini, Laskowski, Vasilakis and
Miller, SIGIR 99.
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Study 3, Sebrechts et al.

• Results (time to locate targets)
• Text clusters fastest
• 2D next
• 3D last
• With practice (6 sessions) 2D neared text
  results 3D still slower
• Computer experts were just as fast with 3D
• Certain tasks equally fast with 2D text
• Find particular cluster
• Find an already-marked document
• But anything involving text (e.g., find title)
  much faster with text.
• Spatial location rotated, so users lost context
• Helpful viz features
• Color coding (helped text too)
• Relative vertical locations

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Clustering Study 4

• Compared several factors
• Findings
• Topic effects dominate (this is a common finding)
• Strong difference in results based on spatial
  ability
• No difference between librarians and other people
• No evidence of usefulness for the cluster
  visualization

Aspect windows, 3-D visualizations, and indirect
comparisons of information retrieval systems,
Swan, Allan, SIGIR 1998.
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SummaryVisualizing for Search Using Clusters

• Huge 2D maps may be inappropriate focus for
  information retrieval
• cannot see what the documents are about
• space is difficult to browse for IR purposes
• (tough to visualize abstract concepts)
• Perhaps more suited for pattern discovery and
  gist-like overviews.

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Clustering Algorithm Problems

• Doesnt work well if data is too homogenous or
  too heterogeneous
• Often is difficult to interpret quickly
• Automatically generated labels are unintuitive
  and occur at different levels of description
• Often the top-level can be ok, but the subsequent
  levels are very poor
• Need a better way to handle items that fall into
  more than one cluster

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Term-based Grouping

• Single Term from Document Characterizes the Group
• (Monothetic)

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Findex, Kaki Aula

• Two innovations
• Used very simple method to create the groupings,
  so that it is not opaque to users
• Based on frequent keywords
• Doc is in category if it contains the keyword
• Allows docs to appear in multiple categories
• Did a naturalistic, longitudinal study of use
• Analyzed the results in interesting ways

• Kaki and Aula Findex Search Result Categories
  Help Users when Document Ranking Fails, CHI 05

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Study Design

• 16 academics
• 8F, 8M
• No CS
• Frequent searchers
• 2 months of use
• Special Log
• 3099 queries issued
• 3232 results accessed
• Two questionnaires (at start and end)
• Google as search engine rank order retained

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After 1 Week After 2 Months
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Kaki Aula Key Findings (all significant)

• Category use takes almost 2 times longer than
  linear
• First doc selected in 24.4 sec vs 13.7 sec
• No difference in average number of docs opened
  per search (1.05 vs. 1.04)
• However, when categories used, users select 1
  doc in 28.6 of the queries (vs 13.6)
• Num of searches without 0 result selections is
  lower when the categories are used
• Median position of selected doc when
• Using categories 22 (sd38)
• Just ranking 2 (sd8.6)

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Kaki Aula Key Findings

• Category Selections
• 1915 categories selections in 817 searches
• Used in 26.4 of the searches
• During the last 4 weeks of use, the proportion of
  searches using categories stayed above the
  average (27-39)
• When categories used, selected 2.3 cats on
  average
• Labels of selected cats used 1.9 words on average
  (average in general was 1.4 words)
• Out of 15 cats (default)
• First quartile at 2nd cat
• Median at 5th
• Third quartile at 9th

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Kaki Aula Survey Results

• Subjective opinions improved over time
• Realization that categories useful only some of
  the time
• Freeform responses indicate that categories
  useful when queries vague, broad or ambiguous
• Second survey indicated that people felt that
  their search habits began to change
• Consider query formulation less than before (27)
• Use less precise search terms (45)
• Use less time to evaluate results (36)
• Use categories for evaluating results (82)

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Conclusions from Kaki Study

• Simplicity of category assignment made groupings
  understandable
• (my view, not stated by them)
• Keyword-based Categories
• Are beneficial when result ranking fails
• Find results lower in the ranking
• Reduce empty results
• May make it easier to access multiple results
• Availability changed user querying behavior

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Category-based Grouping

• General Categories
• Domain-Specific Categories

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DynaCat, Pratt, Hearst, and Fagan.

• Medical Domain
• Decide on important question types in an advance
• What are the adverse effects of drug D?
• What is the prognosis for treatment T?
• Make use of MeSH categories
• Retain only those types of categories known to be
  useful for this type of query.

Pratt, W., Hearst, M, and Fagan, L. A
Knowledge-Based Approach to Organizing Retrieved
Documents. AAAI-99
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DynaCat, Pratt, Hearst, Fagan
Pratt, W., Hearst, M, and Fagan, L. A
Knowledge-Based Approach to Organizing Retrieved
Documents. AAAI-99
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DynaCat Study, Pratt, Hearst Fagan

• 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

Pratt, W., Hearst, M, and Fagan, L. A
Knowledge-Based Approach to Organizing Retrieved
Documents. AAAI-99
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DynaCat study, Pratt et al.
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Faceted Category Navigation

• Multiple Categories per Document

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Search Usability Design Goals

• Strive for Consistency
• Provide Shortcuts
• Offer Informative Feedback
• Design for Closure
• Provide Simple Error Handling
• Permit Easy Reversal of Actions
• Support User Control
• Reduce Short-term Memory Load

From Shneiderman, Byrd, Croft, Clarifying
Search, DLIB Magazine, Jan 1997. www.dlib.org
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How to Structure Information for Search and
Browsing?

• Hierarchy is too rigid
• Full knowledge representation is too complex

• Hierarchical faceted metadata
• A useful middle ground

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The Problem with Hierarchy

• Inflexible
• Force the user to start with a particular
  category
• What if I dont know the animals diet, but the
  interface makes me start with that category?
• Wasteful
• Have to repeat combinations of categories
• Makes for extra clicking and extra coding
• Difficult to modify
• To add a new category type, must duplicate it
  everywhere or change things everywhere

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The Idea of Facets

• Facets are a way of labeling data
• A kind of Metadata (data about data)
• Can be thought of as properties of items
• Facets vs. Categories
• Items are placed INTO a category system
• Multiple facet labels are ASSIGNED TO items

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The Idea of Facets

• Create INDEPENDENT categories (facets)
• Each facet has labels (sometimes arranged in a
  hierarchy)
• Assign labels from the facets to every item
• Example recipe collection

Ingredient
Cooking Method
Chicken
Stir-fry
Bell Pepper
Curry
Course
Cuisine
Main Course
Thai
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The Idea of Facets

• Break out all the important concepts into their
  own facets
• Sometimes the facets are hierarchical
• Assign labels to items from any level of the
  hierarchy

Preparation Method Fry Saute Boil
Bake Broil Freeze
Desserts Cakes Cookies Dairy
Ice Cream Sorbet Flan
Fruits Cherries Berries Blueberries
Strawberries Bananas Pineapple
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Using Facets

• Now there are multiple ways to get to each item

Preparation Method Fry Saute Boil
Bake Broil Freeze
Desserts Cakes Cookies Dairy
Ice Cream Sherbet Flan
Fruits Cherries Berries Blueberries
Strawberries Bananas Pineapple
Fruit Pineapple Dessert Cake Preparation
Bake
Dessert Dairy Sherbet Fruit Berries
Strawberries Preparation Freeze
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Flamenco Usability Studies

• Usability studies done on 3 collections
• Recipes 13,000 items
• Architecture Images 40,000 items
• Fine Arts Images 35,000 items
• Conclusions
• Users like and are successful with the dynamic
  faceted hierarchical metadata, especially for
  browsing tasks
• Very positive results, in contrast with studies
  on earlier iterations.

Yee, K-P., Swearingen, K., Li, K., and Hearst,
M., Faceted Metadata for Image Search and
Browsing, in CHI 2003.
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Flamenco Study Post-Interface Assessments
All significant at poverwhelming
Yee, K-P., Swearingen, K., Li, K., and Hearst,
M., Faceted Metadata for Image Search and
Browsing, in CHI 2003.
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Flamenco Study Post-Test Comparison
Which Interface Preferable For
Faceted
Baseline
Find images of roses Find all works from a given
period Find pictures by 2 artists in same media
Overall Assessment
More useful for your tasks Easiest to use Most
flexible More likely to result in dead
ends Helped you learn more Overall preference
Yee, K-P., Swearingen, K., Li, K., and Hearst,
M., Faceted Metadata for Image Search and
Browsing, in CHI 2003.
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The Advantages of Facets

• Lets the user decide how to start, and how to
  explore and group.
• After refinement, categories that are not
  relevant to the current results disappear.
• Seamlessly integrates keyword search with the
  organizational structure.
• Very easy to expand out (loosen constraints)
• Very easy to build up complex queries.

Hearst, M., Elliott, A., English, J., Sinha, R.,
Swearingen, K., and Yee, P., Finding the Flow in
Web Site Search, Communications of the ACM, 45
(9), September 2002, pp.42-49
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Advantages of Facets

• Cant end up with empty results sets
• (except with keyword search)
• Helps avoid feelings of being lost.
• Easier to explore the collection.
• Helps users infer what kinds of things are in the
  collection.
• Evokes a feeling of browsing the shelves
• Is preferred over standard search for collection
  browsing in usability studies.
• (Interface must be designed properly)

Hearst, M., Elliott, A., English, J., Sinha, R.,
Swearingen, K., and Yee, P., Finding the Flow in
Web Site Search, Communications of the ACM, 45
(9), September 2002, pp.42-49
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Advantages of Facets

• Seamless to add new facets and subcategories
• Seamless to add new items.
• Helps with categorization wars
• Dont have to agree exactly where to place
  something
• Interaction can be implemented using a standard
  relational database.
• May be easier for automatic categorization

Hearst, M., Elliott, A., English, J., Sinha, R.,
Swearingen, K., and Yee, P., Finding the Flow in
Web Site Search, Communications of the ACM, 45
(9), September 2002, pp.42-49
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Creative Facet Visualization

• Aduna Autofocus

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Creative Facet Visualization

• We Feel Fine

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Creative Facet Visualization

• Fathumb mobile search interface
• http//research.microsoft.com/vibe/projects/FaThum
  b.aspx

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Creative Facet Visualization

• Hutchinson et al.

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Summary Grouping Search Results

• Grouping search results seems beneficial in two
  circumstances
• General web search, using transparent labeling
  (monothetic terms) or category labels rather than
  cluster centroids.
• Effects
• Works primarily on ambiguous queries,
• (so used a fraction of the time)
• Promotes relevant results up from below the first
  page of hits
• So important to group the related items together
  visually
• Users tend to select more documents than with
  linear search
• May work even better with meta-search
• Positive subjective responses (small studies)
• Visualization does not work.

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Summary Grouping Search Results

• Grouping search results seems beneficial in two
  circumstances
• Collection navigation with faceted categories
• Multiple angles better than single categories
• searchers turn into browsers
• Becoming commonplace in e-commerce, digital
  libraries, and other kinds of collections
• Extends naturally to tags.
• Positive subjective responses

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Summary Viz and Search

• What works
• Careful attention to details of layout, font,
  color contrast, etc, to reduce clutter
• Excellent relevance
• Color highlighting/boldface for query term
  matches
• Interfaces with sorting of attributes
• What is liked
• Some visualizations of term hit patterns, as in
  TileBars
• Sometimes thumbnails of search results