Search User Interface Design

Search User Interface Design
Dr Max L. Wilson
Mixed Reality Lab
University of Nottingham, UK

Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
Monday, 2 July 12

About Me
Social Media Search

My Research Areas Casual Search

My Framework

Information vs Interaction

Brain Response

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Software Engineering MEng
HCI & Information Science PhD
Web Science and Semantic Web

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UIST
2008

JCDL
2008
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My PhD

Bates, M. J. (1979a). Idea tactics. Journal of
the American Society for Information
Belkin, N. J., Marchetti, P. G., and Cool, C. Science, 30(5):280–289.
(1993). Braque: design of an interface to support
user interaction in information retrieval. Bates, M. J. (1979b). Information search
Information Processing and Management, 29(3): tactics. Journal of the American Society for
325–344. Information Science, 30(4):205–214.

Monday, 2 July 12

My PhD

Wilson, M. L., schraefel, m. c., and White, R. W. (2009). Evaluating advanced
search interfaces using established information-seeking models. Journal of the
American Society for Information Science and Technology, 60(7):1407–1422.

Monday, 2 July 12

Come and Sii what I’ve built

http://mspace.fm/sii
Best JASIST article 2009
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to observe that experience with or of the subject matter was In the post-task interviews, we asked users to informally
important to the information seekers. We also see two very augment their relevance judgments with scores out of 5.
interesting codes appear in this task, which are able to Overall, the mean score for all rated tweets over all three
compliment each other, the first being shared sentiment, tasks was 2.2, indicating a very low relevancy score.
and secondly entertaining. Both of these codes are Individually, the first task, which was temporal in nature,

Social Media Search
subjective in nature, which could be expected a subjective
task. Useful links and experience were also played an
important role in this task. Many participants found this
scored 2.7. The second task, which involved users search
for information regarding purchasing an iPhone, scored a
very low 1.25. The third and final task, which was a

In Tweet Content T1 T2 T3
Experience Someone reporting a personal experience, but not necessarily suggestion / direction. 15 12 13
Direct Someone making a direct recommendation, but not necessarily relaying a personal 3 3 20
Recommendation experience.
Social Knowledge Containing information that is spreading socially, or becoming general knowledge. 7 6 6
Specific Where facts are listed directly in tweets e.g. prices, times etc. 51 10 47
Information
Reflection on Tweet
Entertaining The reader finds them amusing. 1 3 2
Shared Sentiment The reader agrees with the author of the tweet. 1 2 1
Relevant
Time The time is current. 14 0 2
Location The location is relevant to the query. 6 1 40
Trust
Trusted Author The twitter account has a reputation / following. 3 2 6
Trusted Avatar The visual appearance cultivates trust. 2 0 2
Trusted Link A link to a trustworthy recognizable domain. 14 1 7
Links
Actionable Link The user can perform a transaction by using the link (heavily dependent on trust). 9 0 0
Media Link The link is to rich multimedia content. 9 0 0
Useful Link The link provides valuable information content, e.g. authoritative information, educated 61 30 43
reviews, and discussions.
Meta Tweet
Retweeted Lots Its information that others have passed on lots. 4 0 4
Conversation It is part of a series of tweets, and they all need to be useful. 1 4 4

Table 3. The 16 codes and the 6 categories extracted from responses and tweet pairs from the useful tweets. Further, columns 3-5 show how

ICWSM 2011
frequently each was associated with the temporal (T1), subjective (T2) and location-sensitive (T3) tasks.

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Social Media Search

INSERT VIDEO

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D
behaviours documented so far.
4.1 Need-less browsing d

Casual Leisure Search
Much like the desire to pass time at the television, we saw
many examples (some shown in Table 3) of people passing
a

time typically associated with the ‘browsing’ keyword. 5
h
1) ... I’m not even *doing* anything useful... just browsing
eBay aimlessly...
f
2) to do list today: browse the Internet until fasting break o
time.. S
3) ... just got done eating dinner and my family is watch-
ing the football. Rather browse on the laptop
i
4) I’m at the dolphin mall. Just browsing. b
a
Table 3: Example tweets where the browsing activ- d
ity is need-less. f
t
From the collected tweets it is clear that often the inform- s
ation-need in these situations are not only fuzzy, but2010
HCIR typi- W
cally absent. The aim appears to be focused on the activity, t
where the measure of success would be in how much they
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Casual Leisure Search

Springer Book Chapter - Award: Outstanding Author Contribution
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4
14 13
1

5

10 8
11

2

9

12

7
6

9

11

3
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4

1 14

10
8 12 13
15

11

5
2

6
7

9
16

3
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4

1

Input Features
8

12

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4
13
1

5 Control Features
8
11

9

12

7
6

9

11

3
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1

5 Informational Features
10
11

2

9

12

7
6

9

11

3
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14 13
1

Personalisable Features

2

9

12

6

9

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SUI Design Taxonomy

Input Features

Control Features

Informational Features


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SUI Design Taxonomy

Input Features Search box
Query-by-example
Control Features Clusters/Categories
Taxonomies
Informational Features Facets
Social annotations

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Auto-complete/suggest 4.1. INPUT FEATURES 31

(a) Apple – shows lots of contextual informa- (b) Google – prioritising previous searches.
tion and multimedia.

Figure 4.1: Examples of AutoComplete.
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SUI Design Taxonomy

Input Features
Query Suggestions
Control Features Corrections
Sorting
Filters
Groupings

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46
Sorting
4. MODERN SEARCH USER INTERFACES

(a) Sorting in Amazon (b) Sorting in Walmart (c) Sorting in Yahoo!

(d) Tabular sorting in Scan.co.uk.

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SUI Design Taxonomy
Snippets
Input Features Usable Info
Thumbnails
Previews
Control Features
Relevance Info
2D & 3D Viz
Informational Features Guiding numbers
Zero-click answers
Personalisable Features Signposting
Pagination
Social Info
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Usable Information
Figure 4.17: Snippets in Ciao’s search results can be extended using the ‘more’ link.

Figure 4.18: Results in Sainsbury’s groceries search can be added to the shopping basket without having
to leave the search page.

allows searchers to add items to their cart from the SERP, as shown in Figure 4.18. If searchers are
unsure if an item is right for them, however, they can view a page with more information about
each product, and buy from there too. Ciao!, in Figure 4.17, also has a range of usable links in
their results, including links directly to reviews, pricing options, and to the category that an item
belongs in. In Google Image Search, there is a usable link that turns any result into a new search for
‘Similar Images,’ as discussed in the Query-by-example section above. Further, searchers may now
Dr Max L. Wilson ‘+1’ a result in a Google SERP, without affecting or interrupting their search. Finally, searching http://cs.nott.ac.uk/~mlw/
in
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74 4. MODERN SEARCH USER INTERFACES

Social Information
Recommendation

• Track and reuse information about the behaviour of a systems
searchers.

Figure 4.39: Amazon often provides feedback to tell searchers what people typically end up actually
buying.

or even the way they are presented. Further, they can affect the Control features that are provided.
For clariﬁcation, there has been a lot of work that has focused on algorithmic personalisation for
Monday, 2 July 12 search, which has a whole book of its own [133]. Instead, this section focuses on different types of

SUI Design Taxonomy

Input Features

Control Features


Personalisable Features Current-search
Persistent
Socialised
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76 4. MODERN SEARCH USER INTERFACES

Search Histories
Recommendation

• Help searchers to return to previously viewed SERPs and results.

(a) History of searches in PubMed. (b) History of searches and results in Ama-
zon.

Figure 4.41: SUIs can help searchers get back to previous searches by keeping a history.
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SUI Design Taxonomy
Input Control

Informational Personalisable

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The Search Box
Input Control
Query
Only
sb


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The Search Box
Input Control

with
auto- sb

suggest


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The Search Box
Input Control

If query
sb is persistent
in search box


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The Search Box
Input Control
with auto-
suggest,
and query
left in
sb place, and
if auto-
suggest
includes
search
Informational Personalisable history
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The Sweet Spot for SUI design
Input Control


Good SUI features ﬁt into >1 category
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• The Taxonomy

• Historical context

• Lots of examples

• 20 Design Recommendations

• Future Trends

• Evaluation notes
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Does Interaction Matter?

Does interaction provide signiﬁcant beneﬁts to users?

Or is it just more information and more data?

How should companies prioritise investment in these areas?

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Useful info - or Efﬁcient interaction?

• Kelly et al (2009) - query suggests > term suggestions

• Ruthven (2003) - humans not good at choosing useful ones

• Diriye (2009) - slow people down during simple tasks

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Useful data? Efﬁcient
(from good algorithm) interaction?

• Hearst & Pederson (1996) - better task performance

• Pirolli et al (1996) - helped to understand corpus

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Powerful interaction?

or lots of useful data?

• Hearst (2006) - careful metadata is always better than clusters

• Wilson & schraefel (2009) - good for understanding corpus

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Query data

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Clustered
Query data
algorithms
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Clustered Faceted
Query data
algorithms metadata
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Figure 1: The three interaction conditions in the stu
common form. UIC in the middle presents secondar
hierarchical clustering. UIF on the right, which inclu
terms, or facets, that can be applied to or removed fr

- H1: Searchers will be more efficient with more
powerful interaction, using the same metadata, when
completing search tasks.
- H2: Searchers will enjoy more powerful interaction,
Query data despite using the same metadata.
- H3: Searchers will use query recommendations more
when they are presented differently.
Dr Max L. Wilson In order to accept or reject these hypotheses, we designed a
http://cs.nott.ac.uk/~mlw/
Monday, 2 July 12
3x2 repeated-measures study using two independent

3 Conditions

UIQ UIC UIF
Figure 1: The three interaction conditions in the study. UIQ on the left presents query suggestions in their
common form. UIC in the middle presents secondary query suggestions with an interaction model based on
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xperience, standard Two standard types of user study task were used in the
he Bing API for the study: 1) a simple lookup task and 2) an exploratory task.
op-level entities in All six tasks are shown in Table 1.
n, UIC then asked

2 Types of Task
The simple lookup tasks had a fixed answer, but the chosen
h were represented task description was presented in such a way that the most
To create the same likely query would not find the answer without subsequent
wsing through the queries or refinements. This approach was chosen to
ies, the searchers intrinsically encourage participants to use the IIR features
box. As well as on the left of each user interface condition.
n selected in the
dard terminology to Table 1: Tasks set to participants in the study.
em in hierarchy]’. S = Simple, E = Exploratory
technically issuing ID S/E Task Description
xperience appeared 1 S What is the population of Ohio?
fferent sub-clusters
y. 2 E Find an appropriate review of “Harry Potter and
the Deathly Hallows”.
filtering systems,
- Compare the rating with the previous film.
of metadata made
mbination in order 3 S Find the first state of America.
s able to flexibly 4 E Deduce the main problems that Steve Jobs
f keyword filters in incurred with regards to his health.
or and narrow their 5 S What is the iPad 3’s proposed processor name?
typically maintain
6 E Explore information related to Apple’s next
arch box, and then
iPhone, the iPhone 5.
results to portions
- Note the expected release date. There could well
be multiple rumours.
elves to using just
aimed L. Wilson
Dr Max
to create a http://cs.nott.ac.uk/~mlw/
to apply 12
Monday, 2 July multiple The exploratory search tasks were chosen to be tasks with

18 People
Intro + UI1 UI2 UI3 QA +
Consent 2 tasks 2 tasks 2 tasks Debrief

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18 People

Queries
Reﬁnements
Pageviews
Time

Measures
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18 People

Queries
Reﬁnements Ease of use
Pageviews Task Satisfaction
Time

Measures
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18 People

Queries
Quickest
Reﬁnements Ease of use
Most Enjoyable
Pageviews Task Satisfaction
Best Design
Time

Measures
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Simple vs Exploratory

Measure S E Diff

Time 176s 179s no

Queries 1.75 2.33 p<0.05

Pageviews 1.65 2.09 p<0.005

Reﬁnements 2.42 2.45 no

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Log data By UI

Measure Simple Exploratory

Queries UIQ < UIC & UIF UIQ > UIC & UIF

Reﬁnements No diff UIQ & UIC < UIF

Visits No diff UIQ > UIC & UIF

Time UIQ > UIC < UIF UIC < UIF < UIQ

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Subjective Responses
Measure Simple
Easy of Use UIQ & UIC > UIF
Satisfaction UIQ & UIC > UIF

Question UIQ UIC UIF
Quickest to correct answer 11 5 2
Most enjoyed during task 4 11 3
Most appealing design 5 11 2
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What did we actually learn?

• We did see different behaviour in all 3 conditions

• People were good at simple tasks with original UIQ

• People were faster and more effective with UIC
and preferred it

• People used more ﬁlters and viewed fewer pages with UIF
but did not like it so much

• But is it better or worse behaviour?
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Facets

Clusters
Performance

Suggestions

(hypothetically)
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SUI Design + Brain Response

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Cognitive Load Theory

Total Mental Capacity

Easy Task

Simple UI

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Hard Task

Simple UI

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Hard Task

Complex UI

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SUI Design & Brain Response

Clear design recommendations

Cost vs Gain of adding a feature

Ways to reduce cost of a feature

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Search User Interface Design

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