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Designing and Evaluating Search Interfaces

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Title: Designing and Evaluating Search Interfaces


1
Designing and Evaluating Search Interfaces
Prof. Marti Hearst School of Information UC
Berkeley    
2
Outline
  • Why is Supporting Search Difficult?
  • What Works?
  • How to Evaluate?

3
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

4
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)

5
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

6
Vocabulary Disconnect
  • If you ask a set of people to describe a set of
    things there is little overlap in the results.

7
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)
8
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)
9
Lack of Technical Understanding
  • Most people dont understand the underlying
    methods by which search engines work.

10
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.
11
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.
12
What Works?
13
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

14
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

18
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

19
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
20
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

23
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.
24
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
28
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

34
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.

35
Unlikely to notice the function here
36
Scaffolding to teach what is available
37
Query Term Suggestions
38
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
39
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
40
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.
41
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
    25
  • 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.
42
Query Reformulation Study
Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
43
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.
44
Query Reformulation Study
  • Types of refinements

Anick, Using Terminological Feedback for Web
Search Refinement A Log-based Study, SIGIR03.
45
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

46
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

47
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.
48
Searching Over Personal Information
  • Many locations, interfaces for finding things
    (e.g., web, mail, local files, help, history,
    notes)

Slide adapted from Sue Dumais.
49
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.
50
SIS Interface
Slide adapted from Sue Dumais
51
Search With SIS
Slide adapted from Sue Dumais
52
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
53
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
54
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
55
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
56
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
57
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.
73
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

74
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?

85
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

86
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

87
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

88
The Baseline System
  • Floogle
  • Take the best of the existing keyword-based image
    search systems

89
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.

95
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

96
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

97
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
102
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

104
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.

105
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

108
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
109
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

110
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!

111
Thank you!
  • Marti Hearst
  • http//www.ischool.berkeley.edu/hearst

112
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
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