Designing and Evaluating Search Interfaces

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Designing and Evaluating Search Interfaces
Prof. Marti Hearst School of Information UC
Berkeley    
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Outline

• Why is Supporting Search Difficult?
• What Works?
• How to Evaluate?

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Why is Supporting Search Difficult?

• Everything is fair game
• Abstractions are difficult to represent
• The vocabulary disconnect
• Users lack of understanding of the technology
• Clutter vs. Information

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Everything is Fair Game

• The scope of what people search for is all of
  human knowledge and experience.
• Other interfaces are more constrained
• (word processing, formulas, etc)
• Interfaces must accommodate human differences in
• Knowledge / life experience
• Cultural background and expectations
• Reading / scanning ability and style
• Methods of looking for things (pilers vs. filers)

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Abstractions Are Hard to Represent

• Text describes abstract concepts
• Difficult to show the contents of text in a
  visual or compact manner
• Exercise
• How would you show the preamble of the US
  Constitution visually?
• How would you show the contents of Joyces
  Ulysses visually? How would you distinguish it
  from Homers The Odyssey or McCourts Angelas
  Ashes?
• The point it is difficult to show text without
  using text

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Vocabulary Disconnect

• If you ask a set of people to describe a set of
  things there is little overlap in the results.

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The Vocabulary Problem

• Data sets examined (and of participants)
• Main verbs used by typists to describe the kinds
  of edits that they do (48)
• Commands for a hypothetical message decoder
  computer program (100)
• First word used to describe 50 common objects
  (337)
• Categories for 64 classified ads (30)
• First keywords for a each of a set of recipes
  (24)

Furnas, Landauer, Gomez, Dumais The Vocabulary
Problem in Human-System Communication. Commun.
ACM 30(11) 964-971 (1987)
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The Vocabulary Problem

• These are really bad results
• If one person assigns the name, the probability
  of it NOT matching with another persons is about
  80
• What if we pick the most commonly chosen words as
  the standard? Still not good

Furnas, Landauer, Gomez, Dumais The Vocabulary
Problem in Human-System Communication. Commun.
ACM 30(11) 964-971 (1987)
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Lack of Technical Understanding

• Most people dont understand the underlying
  methods by which search engines work.

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People Dont Understand Search Technology

• A study of 100 randomly-chosen people found
• 14 never type a url directly into the address
  bar
• Several tried to use the address bar, but did it
  wrong
• Put spaces between words
• Combinations of dots and spaces
• nursing spectrum.com consumer reports.com
• Several use search form with no spaces
• plumberslocal9 capitalhealthsystem
• People do not understand the use of quotes
• Only 16 use quotes
• Of these, some use them incorrectly
• Around all of the words, making results too
  restrictive
• lactose intolerance recipies
• Here the excludes the recipes
• People dont make use of advanced features
• Only 1 used find in page
• Only 2 used Google cache

Hargattai, Classifying and Coding Online Actions,
Social Science Computer Review 22(2), 2004
210-227.
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People Dont Understand Search Technology

• Without appropriate explanations, most of 14
  people had strong misconceptions about
• ANDing vs ORing of search terms
• Some assumed ANDing search engine indexed a
  smaller collection most had no explanation at
  all
• For empty results for query to be or not to be
• 9 of 14 could not explain in a method that
  remotely resembled stop word removal
• For term order variation boat fire vs. fire
  boat
• Only 5 out of 14 expected different results
• Understanding was vague, e.g.
• Lycos separates the two words and searches for
  the meaning, instead of whatre your looking for.
  Google understands the meaning of the phrase.

Muramatsu Pratt, Transparent Queries
Investigating Users Mental Models of Search
Engines, SIGIR 2001.
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What Works?
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Cool Doesnt Cut It

• Its very difficult to design a search interface
  that users prefer over the standard
• Some ideas have a strong WOW factor
• Examples
• Kartoo
• Groxis
• Hyperbolic tree
• But they dont pass the will you use it test
• Even some simpler ideas fall by the wayside
• Example
• Visual ranking indicators for results set
  listings

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Early Visual Rank Indicators
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Metadata Matters

• When used correctly, text to describe text,
  images, video, etc. works well
• Searchers often turn into browsers with
  appropriate links
• However, metadata has many perils
• The Kosher Recipe Incident

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Small Details Matter

• UIs for search especially require great care in
  small details
• In part due to the text-heavy nature of search
• A tension between more information and
  introducing clutter
• How and where to place things important
• People tend to scan or skim
• Only a small percentage reads instructions

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Small Details Matter

• UIs for search especially require endless tiny
  adjustments
• In part due to the text-heavy nature of search
• Example
• In an earlier version of the Google Spellchecker,
  people didnt always see the suggested correction
• Used a long sentence at the top of the page
• If you didnt find what you were looking for
  
• People complained they got results, but not the
  right results.
• In reality, the spellchecker had suggested an
  appropriate correction.

Interview with Marissa Mayer by Mark Hurst
http//www.goodexperience.com/columns/02/1015googl
e.html
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Small Details Matter

• The fix
• Analyzed logs, saw people didnt see the
  correction
• clicked on first search result,
• didnt find what they were looking for (came
  right back to the search page
• scrolled to the bottom of the page, did not find
  anything
• and then complained directly to Google
• Solution was to repeat the spelling suggestion at
  the bottom of the page.
• More adjustments
• The message is shorter, and different on the top
  vs. the bottom

Interview with Marissa Mayer by Mark Hurst
http//www.goodexperience.com/columns/02/1015googl
e.html
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Small Details Matter

• Layout, font, and whitespace for
  information-centric interfaces requires very
  careful design
• Example
• Photo thumbnails
• Search results summaries

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What Works for Search Interfaces?

• Query term highlighting
• in results listings
• in retrieved documents
• Term Suggestions (if done right)
• Sorting of search results according to important
  criteria (date, author)
• Grouping of results according to well-organized
  category labels (see Flamenco)
• DWIM only if highly accurate
• Spelling correction/suggestions
• Simple relevance feedback (more-like-this)
• Certain types of term expansion
• So far not really visualization

Hearst et al Finding the Flow in Web Site
Search, CACM 45(9), 2002.
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Highlighting Query Terms

• Boldface or color
• Adjacency of terms with relevant context is a
  useful cue.

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Highlighted query term hits using Google toolbar
Microso
US
Blackout
PGA
Microsoft
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How to Introduce New Features?

• Example Yahoo shortcuts
• Search engines now provide groups of enriched
  content
• Automatically infer related information, such as
  sports statistics
• Accessed via keywords
• User can quickly specify very specific
  information
• united 570 (flight arrival time)
• map san francisco
• Were heading back to command languages!

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Introducing New Features

• A general technique scaffolding
• Scaffolding
• Facilitate a students ability to build on prior
  knowledge and internalize new information.
• The activities provided in scaffolding
  instruction are just beyond the level of what the
  learner can do already.
• Learning the new concept moves the learner up one
  step on the conceptual ladder

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Scaffolding Example

• The problem how do people learn about these
  fantastic but unknown options?
• Example scaffolding the definition function
• Where to put a suggestion for a definition?
• Google used to simply hyperlink it next to the
  statistics for the word.
• Now a hint appears to alert people to the
  feature.

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Unlikely to notice the function here
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Scaffolding to teach what is available
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Query Term Suggestions
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Query Reformulation

• Query reformulation
• After receiving unsuccessful results, users
  modify their initial queries and submit new ones
  intended to more accurately reflect their
  information needs.
• Web search logs show that searchers often
  reformulate their queries
• A study of 985 Web user search sessions found
• 33 went beyond the first query
• Of these, 35 retained the same number of terms
  while 19 had 1 more term and 16 had 1 fewer

Use of query reformulation and relevance feedback
by Excite users, Spink, Janson Ozmultu,
Internet Research 10(4), 2001
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Query Reformulation

• Many studies show that if users engage in
  relevance feedback, the results are much better.
• In one study, participants did 17-34 better with
  RF
• They also did better if they could see the RF
  terms than if the system did it automatically
  (DWIM)
• But the effort required for doing so is usually a
  roadblock.
• Before the web and in most research, searches
  have to select MANY relevant documents or MANY
  terms.

Koenemann Belkin, A Case for Interaction A
Study of Interactive Information Retrieval
Behavior and Effectiveness, CHI96
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Query Reformulation

• What happens when the web search engines suggests
  new terms?
• Web log analysis study using the Prisma term
  suggestion system

Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
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Query Reformulation Study

• Feedback terms were displayed to 15,133 user
  sessions.
• Of these, 14 used at least one feedback term
• For all sessions, 56 involved some degree of
  query refinement
• Within this subset, use of the feedback terms was
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• By user id, 16 of users applied feedback terms
  at least once on any given day
• Looking at a 2-week session of feedback users
• Of the 2,318 users who used it once, 47 used it
  again in the same 2-week window.
• Comparison was also done to a baseline group that
  was not offered feedback terms.
• Both groups ended up making a page-selection
  click at the same rate.

Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
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Query Reformulation Study
Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
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Query Reformulation Study

• Other observations
• Users prefer refinements that contain the initial
  query terms
• Presentation order does have an influence on term
  uptake

Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
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Query Reformulation Study

• Types of refinements

Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
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Prognosis Query Reformulation

• Researchers have always known it can be helpful,
  but the methods proposed for user interaction
  were too cumbersome
• Had to select many documents and then do feedback
• Had to select many terms
• Was based on statistical ranking methods which
  are hard for people to understand
• RF is promising for web-based searching
• The dominance of AND-based searching makes it
  easier to understand the effects of RF
• Automated systems built on the assumption that
  the user will only add one term now work
  reasonably well
• This kind of interface is simple

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Supporting the Search Process

• We should differentiate among searching
• The Web
• Personal information
• Large collections of like information
• Different cues useful for each
• Different interfaces needed
• Examples
• The Stuff Ive Seen Project
• The Flamenco Project

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The Stuff Ive Seen project

• Did intense studies of how people work
• Used the results to design an integrated search
  framework
• Did extensive evaluations of alternative designs
• The following slides are modifications of ones
  supplied by Sue Dumais, reproduced with
  permission.

Dumais, Cutrell, Cadiz, Jancke, Sarin and
Robbins, Stuff I've Seen A system for personal
information retrieval and re-use.  SIGIR 2003.
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Searching Over Personal Information

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

Slide adapted from Sue Dumais.
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The Stuff Ive Seen project

• Unified index of items touched recently by user
• All types of information, e.g., files of all
  types, email, calendar, contacts, 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
• Search only over things already seen
• Re-use vs. initial discovery

Slide adapted from Sue Dumais.
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SIS Interface
Slide adapted from Sue Dumais
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Search With SIS
Slide adapted from Sue Dumais
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Evaluating SIS

• Internal deployment
• 1500 downloads
• Users include program management, test, sales,
  development, administrative, executives, etc.
• Research techniques
• Free-form feedback
• Questionnaires Structured interviews
• Usage patterns from log data
• UI experiments (randomly deploy different
  versions)
• Lab studies for richer UI (e.g., timeline,
  trends)
• But even here must work with users own content

Slide adapted from Sue Dumais
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SIS Usage Data

• Detailed analysis for 234 people, 6 weeks usage
• Personal store characteristics
• 5k 100k items index lt150 meg
• Query characteristics
• Short queries (1.59 words)
• Few advanced operators or fielded search in query
  box (7.5)
• Frequent use of query iteration (48)
• 50 refined queries involve filters type, date
  most common
• 35 refined queries involve changes to query
• 13 refined queries involve re-sort
• Query content
• Importance of people
• 29 of the queries involve peoples names

Slide adapted from Sue Dumais
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SIS Usage Data, contd

• Characteristics of items opened
• File types opened
• 76 Email
• 14 Web pages
• 10 Files
• Age of items opened
• 7 today
• 22 within the last week
• 46 within the last month
• Ease of finding information
• Easier after SIS for web, email, files
• Non-SIS search decreases for web, email, files

Log(Freq) -0.68 log(DaysSinceSeen) 2.02
Slide adapted from Sue Dumais
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SIS Usage, contd

• UI Usage
• Small effects of Top/Side, Previews
• Sort order
• Date by far the most common sort field, even for
  people who had Okapi Rank as default
• Importance of time
• Few searches for best match many other
  criteria

Number of Queries Issued
Slide adapted from Sue Dumais
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Web Sites and Collections

• A report by Forrester research in 2001 showed
  that while 76 of firms rated search as
  extremely important only 24 consider their Web
  sites search to be extremely useful.

Johnson, K., Manning, H., Hagen, P.R., and
Dorsey, M. Specialize Your Site's Search.
Forrester Research, (Dec. 2001), Cambridge, MA
www.forrester.com/ER/Research/Report/Summary/0,133
8,13322,00
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There are many ways to do it wrong

• Examples
• Melvyl online catalog
• no way to browse enormous category listings
• Audible.com, BooksOnTape.com, and
  BrillianceAudio
• no way to browse a given category and
  simultaneosly select unabridged versions
• Amazon.com
• has finally gotten browsing over multiple kinds
  of features working this is a recent development
• but still restricted on what can be added into
  the query

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The Flamenco Project

• Incorporating Faceted Hierarchical Metadata into
  Interfaces for Large Collections
• Key Goals
• Support integrated browsing and keyword search
• Provide an experience of browsing the shelves
• Add power and flexibility without introducing
  confusion or a feeling of clutter
• Allow users to take the path most natural to them
• Method
• User-centered design, including needs assessment
  and many iterations of design and testing

Yee, Swearingen, Li, Hearst, Faceted Metadata for
Image Search and Browsing, Proceedings of CHI
2003.
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Some Challenges

• Users dont like new search interfaces.
• How to show lots more information without
  overwhelming or confusing?
• Our approach
• Integrate the search seamlessly into the
  information architecture.
• Use proper HCI methodologies.
• Use faceted metadata

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The Flamenco Interface

• Hierarchical facets
• Chess metaphor
• Opening
• Middle game
• End game
• Tightly Integrated Search
• Expand as well as Refine
• Intermediate pages for large categories
• For this design, small details really matter

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What is Tricky About This?

• It is easy to do it poorly
• Yahoo directory structure
• It is hard to be not overwhelming
• Most users prefer simplicity unless complexity
  really makes a difference
• It is hard to make it flow
• Can it feel like browsing the shelves?

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Using HCI Methodology

• Identify Target Population
• Architects, city planners
• Needs assessment.
• Interviewed architects and conducted contextual
  inquiries.
• Lo-fi prototyping.
• Showed paper prototype to 3 professional
  architects.
• Design / Study Round 1.
• Simple interactive version. Users liked metadata
  idea.
• Design / Study Round 2
• Developed 4 different detailed versions
  evaluated with 11 architects results somewhat
  positive but many problems identified. Matrix
  emerged as a good idea.
• Metadata revision.
• Compressed and simplified the metadata
  hierarchies

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Using HCI Methodology

• Design / Study Round 3.
• New version based on results of Round 2
• Highly positive user response
• Identified new user population/collection
• Students and scholars of art history
• Fine arts images
• Study Round 4
• Compare the metadata system to a strong,
  representative baseline

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Most Recent Usability Study

• Participants Collection
• 32 Art History Students
• 35,000 images from SF Fine Arts Museum
• Study Design
• Within-subjects
• Each participant sees both interfaces
• Balanced in terms of order and tasks
• Participants assess each interface after use
• Afterwards they compare them directly
• Data recorded in behavior logs, server logs,
  paper-surveys one or two experienced testers at
  each trial.
• Used 9 point Likert scales.
• Session took about 1.5 hours pay was 15/hour

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The Baseline System

• Floogle
• Take the best of the existing keyword-based image
  search systems

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Comparison of Common Image Search Systems
System Collection Results /page Categories? Familiar
Google Web 20 No 27
AltaVista Web 15 No 8
Corbis Photos 9-36 No 8
Getty Photos, Art 12-90 Yes 6
MS Office Photos, Clip art 6-100 Yes N/A
Thinker Fine arts images 10 Yes 4
BASELINE Fine arts images 40 Yes N/A
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sword
sword
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Evaluation Quandary

• How to assess the success of browsing?
• Timing is usually not a good indicator
• People often spend longer when browsing is going
  well.
• Not the case for directed search
• Can look for comprehensiveness and correctness
  (precision and recall)
• But subjective measures seem to be most
  important here.

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Hypotheses

• We attempted to design tasks to test the
  following hypotheses
• Participants will experience greater search
  satisfaction, feel greater confidence in the
  results, produce higher recall, and encounter
  fewer dead ends using FC over Baseline
• FC will perceived to be more useful and flexible
  than Baseline
• Participants will feel more familiar with the
  contents of the collection after using FC
• Participants will use FC to create multi-faceted
  queries

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Four Types of Tasks

• Unstructured (3) Search for images of interest
• Structured Task (11-14) Gather materials for an
  art history essay on a given topic, e.g.
• Find all woodcuts created in the US
• Choose the decade with the most
• Select one of the artists in this periods and
  show all of their woodcuts
• Choose a subject depicted in these works and find
  another artist who treated the same subject in a
  different way.
• Structured Task (10) compare related images
• Find images by artists from 2 different countries
  that depict conflict between groups.
• Unstructured (5) search for images of interest

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Other Points

• Participants were NOT walked through the
  interfaces.
• The wording of Task 2 reflected the metadata not
  the case for Task 3
• Within tasks, queries were not different in
  difficulty (tslt1.7, p gt0.05 according to
  post-task questions)
• Flamenco is and order of magnitude slower than
  Floogle on average.
• In task 2 users were allowed 3 more minutes in FC
  than in Baseline.
• Time spent in tasks 2 and 3 were significantly
  longer in FC (about 2 min more).

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Results

• Participants felt significantly more confident
  they had found all relevant images using FC (Task
  2 t(62)2.18, plt.05 Task 3 t(62)2.03, plt.05)
• Participants felt significantly more satisfied
  with the results
• (Task 2 t(62)3.78, plt.001 Task 3 t(62)2.03,
  plt.05)
• Recall scores
• Task2a In Baseline 57 of participants found all
  relevant results, in FC 81 found all.
• Task 2b In Baseline 21 found all relevant, in
  FC 77 found all.

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Post-Interface Assessments
All significant at plt.05 except simple and
overwhelming
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Perceived Uses of Interfaces
Baseline
FC
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Post-Test Comparison
FC
Baseline
Which Interface Preferable For
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
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Facet Usage

• Facets driven largely by task content
• Multiple facets 45 of time in structured tasks
• For unstructured tasks,
• Artists (17)
• Date (15)
• Location (15)
• Others ranged from 5-12
• Multiple facets 19 of time
• From end game, expansion from
• Artists (39)
• Media (29)
• Shapes (19)

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Qualitative Observations

• Baseline
• Simplicity, similarity to Google a plus
• Also noted the usefulness of the category links
• FC
• Starting page well-organized, gave ideas for
  what to search for
• Query previews were commented on explicitly by 9
  participants
• Commented on matrix prompting where to go next
• 3 were confused about what the matrix shows
• Generally liked the grouping and organizing
• End game links seemed useful 9 explicitly
  remarked positively on the guidance provided
  there.
• Often get requests to use the system in future

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Study Results Summary

• Overwhelmingly positive results for the faceted
  metadata interface.
• Somewhat heavy use of multiple facets.
• Strong preference over the current state of the
  art.
• This result not seen in similarity-based image
  search interfaces.
• Hypotheses are supported.

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Summary

• 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
• Note it seems you have to care about the
  contents of the collection to like the interface

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Using DWIM

• DWIM Do What I Mean
• Refers to systems that try to be smart by
  guessing users unstated intentions or desires
• Examples
• Automatically augment my query with related terms
• Automatically suggest spelling corrections
• Automatically load web pages that might be
  relevant to the one Im looking at
• Automatically file my incoming email into folders
• Pop up a paperclip that tells me what kind of
  help I need.
• THE CRITICAL POINT
• Users love DWIM when it really works
• Users DESPISE it when it doesnt
• unless not very intrusive

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DWIM that Works

• Amazons customers who bought X also bought Y
• And many other recommendation-related features

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DWIM Example Spelling Correction/Suggestion

• Googles spelling suggestions are highly accurate
• But this wasnt always the case.
• Google introduced a version that wasnt very
  accurate. People hated it. They pulled it.
  (According to a talk by Marissa Mayer of Google.)
• Later they introduced a version that worked well.
  People love it.
• But dont get too pushy.
• For a while if the user got very few results, the
  page was automatically replaced with the results
  of the spelling correction
• This was removed, presumably due to negative
  responses

Information from a talk by Marissa Mayer of Google
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What Weve Covered

• Introduction
• Why is designing for search difficult?
• How to Design for Search
• HCI and iterative design
• What works?
• Small details matter
• Scaffolding
• The Role of DWIM
• Core Problems
• Query specification and refinement
• Browsing and searching collections

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Final Words

• User interfaces for search remains a fascinating
  and challenging field
• Search has taken a primary role in the web and
  internet business
• Thus, we can continue to expect fascinating
  developments, and maybe some breakthroughs, in
  the next few years!

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Thank you!

• Marti Hearst
• http//www.ischool.berkeley.edu/hearst

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References

• Anick, Using Terminological Feedback for Web
  Search Refinement A Log-based Study, SIGIR03.
• Bates, The Berry-Picking Search UI Design, in
  User Interface Design, Thimbley (ED),
  Addison-Wesley 1990
• Chen, Houston, Sewell, and Schatz, JASIS 49(7)
• Chen and Yu, Empirical studies of information
  visualization a meta-analysis, IJHCS 53(5),2000
• Dumais, Cutrell, Cadiz, Jancke, Sarin and
  Robbins, Stuff I've Seen A system for personal
  information retrieval and re-use.  SIGIR 2003.
• Furnas, Landauer, Gomez, Dumais The Vocabulary
  Problem in Human-System Communication. Commun.
  ACM 30(11) 964-971 (1987)
• Hargattai, Classifying and Coding Online Actions,
  Social Science Computer Review 22(2), 2004
  210-227.
• Hearst, English, Sinha, Swearingen, Yee. Finding
  the Flow in Web Site Search, CACM 45(9), 2002.
• Hearst, User Interfaces and Visualization,
  Chapter 10 of Modern Information Retrieval,
  Baeza-Yates and Rebeiro-Nato (Eds),
  Addison-Wesley 1999.
• Johnson, Manning, Hagen, and Dorsey. Specialize
  Your Site's Search. Forrester Research, (Dec.
  2001), Cambridge, MA

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References

• Koenemann Belkin, A Case for Interaction A
  Study of Interactive Information Retrieval
  Behavior and Effectiveness, CHI96
• Marissa Mayer Interview by Mark Hurst
  http//www.goodexperience.com/columns/02/1015googl
  e.html
• Muramatsu Pratt, Transparent Queries
  Investigating Users Mental Models of Search
  Engines, SIGIR 2001.
• ODay Jeffries, Orienteering in an information
  landscape how information seekers get from here
  to there, Proceedings of InterCHI 93.
• Rose Levinson, Understanding User Goals in Web
  Search, Proceedings of WWW04
• Russell, Stefik, Pirolli, Card, The Cost
  Structure of Sensemaking , Proceedings of
  InterCHI 93.
• Sebrechts, Cugini, Laskowski, Vasilakis and
  Miller, Visualization of search results a
  comparative evaluation of text, 2D, and 3D
  interfaces, SIGIR 99.
• Swan and Allan, Aspect windows, 3-D
  visualizations, and indirect comparisons of
  information retrieval systems, SIGIR 1998.
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