A talk given at the 2012 Information Interaction in Context conference (IIiX2012) where we developed 3 alternative versions of Google with 3 different refinement interactions on the left. Each used the same metadata, and the study was designed to show that users can get achieve better performance with different interaction over the same metadata.
You can provide benefits for searchers just be adjusting the interaction to your metadata. You do not _require_ better metadata to get better interaction.
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
IIiX2012 - Information vs Interaction - Examining different interaction models over consistent metadata
1. Information vs Interaction
examining different interaction models over
consistent metadata
(for now)
Kingsley Hughes-Morgan Dr Max L. Wilson
Future Interaction Technology Lab Mixed Reality Lab
Swansea University, UK University of Nottingham, UK
kingsleyhm@googlemail.com max.wilson@nottingham.ac.uk
@kingsleyhm @gingdottwit
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
2. Motivation
Related Work
Information vs Interaction
Design Results Discussion
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
3. Motivation 1
Understanding Search User Interface Design
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
11. Wilson, M. L., Andre, P. and schraefel, m. c. (2008) Backward Highlighting: Enhancing Faceted Search. In:Proceedings of the 21st
Symposium on User Interface Software and Technology (UIST2008), October 19-22, 2008, Monterey, CA, USA. pp. 235-238.
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
13. Better interaction or Better information
Query Suggestions Clustered Categories Faceted Filters
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
14. Our questions
What matters most? Interaction or Information?
!
Given!a!specific!form!of!
metadata,!can!we!recreate!more!
advanced!IIR!interface!features!
such!that!searchers!can!still!
experience!their!benefits?!!
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
19. Our questions
What matters most? Interaction or Information?
!
Given!a!specific!form!of!
metadata,!can!we!recreate!more!
advanced!IIR!interface!features!
such!that!searchers!can!still!
experience!their!benefits?!!
How should companies prioritise investment?
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
20. Motivation
Related Work
Information vs Interaction
Design Results Discussion
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
21. Related Work
Query Suggestions Clustered Categories Faceted Filters
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
22. Related Work
Query Suggestions
• Kelly
et al (2009) - query suggestions are
better than term suggestions
• Ruthven (2003) - humans not good at
choosing useful queries - algorithms should
pick them well.
• Diriye
(2009) - slow people down during
simple tasks
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
23. Related Work
Query Suggestions Clustered Categories Faceted Filters
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
24. Related Work
Clustered Categories
• Hearst & Pederson (1996)
- better task performance
• Pirolli
et al (1996) - helped
to understand corpus
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
25. Related Work
Query Suggestions Clustered Categories Faceted Filters
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
26. Related Work
Faceted Filters
• Hearst (2006) - careful metadata
is always better than clusters
• Wilson & schraefel (2009) -
good for understanding corpus
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
27. Related Work
(Different Interaction and Different Information)
• Joho et al (2006) -
hierarchy better than
linear list
• butused different data
structures
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
28. Different Interaction Same Information
Query Suggestions Clustered Categories Faceted Filters
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
29. Different Interaction Same Information
Clustered Faceted
Query data
algorithms metadata
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
31. Motivation
Related Work
Information vs Interaction
Design Results Discussion
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
32. Our Hypotheses
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,"despite"
using"the"same"metadata.
"
H3:"Searchers"will"use"query"recommendations"more"when"
they"are"presented"differently."
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
33. The 3 Interaction Models
Query Clustered Faceted
Suggestions Categories Filters
Changes every No change on No change on
Query
search refinement refinement
Stay the same Stay the same
Filters New filters
for the query for the query
One at a time Applying filters
Experience Searching again
in Hierarchy in combination
Subset of the Subset of the
Results New results
original results original results
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
34. 3 Conditions
UIQ UIC UIF
Figure 1: The three interaction conditions in the study. UIQ on the left presents query suggestions in their
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
35. Two standard types of user study task were used in the
study: 1) a simple lookup task and 2) an exploratory task.
All six tasks are shown in Table 1.
The simple lookup tasks had a fixed answer, but the chosen
2 Types of Task
task description was presented in such a way that the most
likely query would not find the answer without subsequent
queries or refinements. This approach was chosen to
intrinsically encourage participants to use the IIR features
on the left of each user interface condition.
Table 1: Tasks set to participants in the study.
S = Simple, E = Exploratory
ID S/E Task Description
1 S What is the population of Ohio? A&simple&lookup&task"="had"a"fixed"
2 E Find an appropriate review of “Harry Potter and answer,"subsequent"queries"or"
the Deathly Hallows”. refinements"were"needed.
- Compare the rating with the previous film.
3 S Find the first state of America. An&exploratory&task"="multiple"
4 E Deduce the main problems that Steve Jobs subBproblems,"required"a"series"
incurred with regards to his health. of"searches/refinements"to"
5 S What is the iPad 3’s proposed processor name? combine"answers"from"several"
6 E Explore information related to Apple’s next websites."No"fixed"answer."
iPhone, the iPhone 5. (Collection8style)
- Note the expected release date. There could well
be multiple rumours.
The exploratory search tasks were chosen to be tasks with
multiple sub-problems, such that searchers would have to
perform a series of searches or refinements to combine
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
36. 18 people
16-55 (avg 28)
Mix of students,
All daily web users academic and
non-academic staff
in different schools
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
37. 18 People
Intro + UI1 UI2 UI3 QA +
Consent 2 tasks 2 tasks 2 tasks Debrief
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
38. 18 People
Intro + UI1 UI2 UI3 QA +
Consent 2 tasks 2 tasks 2 tasks Debrief
Queries
Refinements
Pageviews
Time
Measures
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
39. 18 People
Intro + UI1 UI2 UI3 QA +
Consent 2 tasks 2 tasks 2 tasks Debrief
Queries
Refinements Ease of use
Pageviews Task Satisfaction
Time
Measures
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
40. 18 People
Intro + UI1 UI2 UI3 QA +
Consent 2 tasks 2 tasks 2 tasks Debrief
Queries
Quickest
Refinements Ease of use
Most Enjoyable
Pageviews Task Satisfaction
Best Design
Time
Measures
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
41. Motivation
Related Work
Information vs Interaction
Design Results Discussion
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
42. 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
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
43. between the three interface conditions. These results
indicate that for simple tasks, or tasks with a fixed answer,
the different interaction models did not create a significant
Simple tasks
effect on refinement behaviour. Participants did, however,
perform significantly faster in the hierarchical clustering
UIC condition (p<0.005, F(51,2)=6.53), where a post-hoc
TukeyHSD showed that UIF and UIQ were not
significantly different from each other.
Table 2: log data for simple tasks (*=significant)
Mean (std) UIQ UIC UIF
Queries (#) * 1.22 (0.55) 2.11 (1.13) 1.94 (0.93) p<0.05
Refinements (#) 2.44 (0.70) 2.5 (1.95) 2.33 (1.19)
Page visits (#) 1.94 (1.11) 1.61 (0.69) 1.39 (0.61)
Time (s) * 189 (3.15) 154 (2.57) 184 (3.07) p<0.05
It is not clear exactly why participants submitted
significantly more queries in the UIC and UIF conditions,
but the results indicate that participants were fastest when
interacting with a hierarchy. Although we didn’t reach
statistical significance, there was a downward trend to
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
44. between the three interface conditions. These results
indicate that for simple tasks, or tasks with a fixed answer,
the different interaction models did not create a significant
Simple tasks
effect on refinement behaviour. Participants did, however,
perform significantly faster in the hierarchical clustering
UIC condition (p<0.005, F(51,2)=6.53), where a post-hoc
TukeyHSD showed that UIF and UIQ were not
significantly different from each other.
Table 2: log data for simple tasks (*=significant)
Mean (std) UIQ UIC UIF
Queries (#) * 1.22 (0.55) 2.11 (1.13) 1.94 (0.93) p<0.05
Refinements (#) 2.44 (0.70) p<0.05
2.5 (1.95) 2.33 (1.19)
Page visits (#) 1.94 (1.11) p=0.051.39 (0.61)
1.61 (0.69) (!)
Time (s) * 189 (3.15) 154 (2.57) 184 (3.07) p<0.05
It is not clear exactly why participants submitted
significantly more queries in the UIC and UIF conditions,
but the results indicate that participants were fastest when
interacting with a hierarchy. Although we didn’t reach
statistical significance, there was a downward trend to
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
45. between the three interface conditions. These results
indicate that for simple tasks, or tasks with a fixed answer,
the different interaction models did not create a significant
Simple tasks
effect on refinement behaviour. Participants did, however,
perform significantly faster in the hierarchical clustering
UIC condition (p<0.005, F(51,2)=6.53), where a post-hoc
TukeyHSD showed that UIF and UIQ were not
significantly different from each other.
Table 2: log data for simple tasks (*=significant)
Mean (std) UIQ UIC UIF
Queries (#) * 1.22 (0.55) 2.11 (1.13) 1.94 (0.93) p<0.05
Refinements (#) 2.44 (0.70) 2.5 (1.95) 2.33 (1.19)
Page visits (#) 1.94 (1.11) p<0.05(0.69)
1.61 p<0.05
1.39 (0.61)
Time (s) * 189 (3.15) 154 (2.57) 184 (3.07) p<0.05
It is not clear exactly why participants submitted
significantly more queries in the UIC and UIF conditions,
but the results indicate that participants were fastest when
interacting with a hierarchy. Although we didn’t reach
statistical significance, there was a downward trend to
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
46. between the three interface conditions. These results
indicate that for simple tasks, or tasks with a fixed answer,
the different interaction models did not create a significant
Simple tasks
effect on refinement behaviour. Participants did, however,
perform significantly faster in the hierarchical clustering
UIC condition (p<0.005, F(51,2)=6.53), where a post-hoc
TukeyHSD showed that UIF and UIQ were not
significantly different from each other.
Table 2: log data for simple tasks (*=significant)
Mean (std) UIQ UIC UIF
Queries (#) * 1.22 (0.55) 2.11 (1.13) 1.94 (0.93) p<0.05
Refinements (#) 2.44 (0.70) 2.5 (1.95) 2.33 (1.19)
Page visits (#) 1.94 (1.11) 1.61 (0.69) 1.39 (0.61)
Time (s) * 189 (3.15) 154 (2.57) 184 (3.07) p<0.05
It is not clear exactly why participants submitted
significantly more queries in the UIC - p=~0.1
“Downward Trend” and UIF conditions,
but the results indicate that participants were fastest when
interacting with a hierarchy. Although we didn’t reach
statistical significance, there was a downward trend to
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
47. between the three interface conditions. These results
indicate that for simple tasks, or tasks with a fixed answer,
the different interaction models did not create a significant
Simple tasks
effect on refinement behaviour. Participants did, however,
perform significantly faster in the hierarchical clustering
UIC condition (p<0.005, F(51,2)=6.53), where a post-hoc
TukeyHSD showed that UIF and UIQ were not
significantly different from each other.
Table 2: log data for simple tasks (*=significant)
Mean (std) UIQ UIC UIF
Queries (#) * 1.22 (0.55) 2.11 (1.13) 1.94 (0.93) p<0.05
Refinements (#) 2.44 (0.70) 2.5 (1.95) 2.33 (1.19)
Page visits (#) 1.94 (1.11) 1.61 (0.69) 1.39 (0.61)
Time (s) * 189 (3.15) 154 (2.57) 184 (3.07) p<0.05
It - Not much difference in #refinements and #pagevisits
is not clear exactly why participants submitted
significantly more queries ininteractive conditions
- More queries (!) in the UIC and UIF conditions,
but the results indicate that participants were fastest when
- Faster in UIC
interacting with a hierarchy. Although we didn’t reach
statistical significance, there was a downward trend to
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
48. refinements in the faceted UIF condition (F(51,2)=6.245, The choices high
p<0.005). Again, a post-hoc TukeyHSD revealed enjoyed and pref
significant differences between UIF and the two despite believing
Exploratory tasks
alternatives (both p<0.05), but no difference between UIC
and UIQ. Together, these two sets of results indicate that
baseline conditio
timing data, indi
participants behaved very differently in the three baseline felt fast
conditions, for exploratory tasks, using significantly fewer favourably in any
queries (with UIC) and significantly more refinements.
Table 5: Freq
Table 3: log data for exploratory tasks (*=significant) three co
Mean (std) UIQ UIC UIF Frequency of ch
Queries (#) * 3.11 (1.49) 1.44 (0.51) 2.44 (1.29) p<0.0005 to correct
Quickest
Refinements (#) * 2.17 (0.86) 1.78 (0.65) 3.39 (2.23) p<0.005enjoyment du
Most
Page visits (#) * 2.55 (1.04) 1.61 (0.69) 2.11 (0.75) p<0.01 appealing des
Most
Time on task (s) * 190 (3.17) 169 (2.82) 177 (2.95) p<0.01
In exploratory tasks, participants visited significantly more
5. DISCUSSI
Our study has p
pages in the original condition (F(51,2)=5.615, p<0.01),
research question
where a post-hoc TukeyHSD saw only one key difference
support searcher
between UIQ and UIC. This finding may indicate that
have a fixed form
participants were able to find more relevant pages earlier in
Dr Max L. Wilson but hard to find a
http://cs.nott.ac.uk/~mlw/
49. refinements in the faceted UIF condition (F(51,2)=6.245, The choices high
p<0.005). Again, a post-hoc TukeyHSD revealed enjoyed and pref
significant differences between UIF and the two despite believing
Exploratory tasks
alternatives (both p<0.05), but no difference between UIC
and UIQ. Together, these two sets of results indicate that
baseline conditio
timing data, indi
participants behaved very differently in the three baseline felt fast
conditions, for exploratory tasks, using significantly fewer favourably in any
queries (with UIC) and significantly more refinements.
Table 5: Freq
Table 3: log data for exploratory tasks (*=significant) three co
Mean (std) UIQ UIC UIF Frequency of ch
Queries (#) * 3.11 (1.49) 1.44 (0.51) 2.44 (1.29) p<0.0005 to correct
Quickest
Refinements (#) * 2.17 (0.86) 1.78 (0.65) 3.39 (2.23) p<0.005enjoyment du
Most
Page visits (#) * p<0.0005 (0.69)p<0.05 (0.75)
2.55 (1.04) 1.61 2.11 p<0.01 appealing des
Most
Time on task (s) * 190 (3.17) 169 (2.82) 177 (2.95) p<0.01
In exploratory tasks, participants visited significantly more
5. DISCUSSI
Our study has p
pages in the original condition (F(51,2)=5.615, p<0.01),
research question
where a post-hoc TukeyHSD saw only one key difference
support searcher
between UIQ and UIC. This finding may indicate that
have a fixed form
participants were able to find more relevant pages earlier in
Dr Max L. Wilson but hard to find a
http://cs.nott.ac.uk/~mlw/
50. refinements in the faceted UIF condition (F(51,2)=6.245, The choices high
p<0.005). Again, a post-hoc TukeyHSD revealed enjoyed and pref
significant differences between UIF and the two despite believing
Exploratory tasks
alternatives (both p<0.05), but no difference between UIC
and UIQ. Together, these two sets of results indicate that
baseline conditio
timing data, indi
participants behaved very differently in the three baseline felt fast
conditions, for exploratory tasks, using significantly fewer favourably in any
queries (with UIC) and significantly more refinements.
Table 5: Freq
Table 3: log data for exploratory tasks (*=significant) three co
Mean (std) UIQ UIC UIF Frequency of ch
Queries (#) * 3.11 (1.49) 1.44 (0.51) 2.44 (1.29)
p<0.05 p<0.0005 to correct
Quickest
Refinements (#) * 2.17 (0.86) 1.78 (0.65) 3.39 (2.23) p<0.005enjoyment du
Most
Page visits (#) * 2.55 (1.04) 1.61 (0.69) 2.11 (0.75) p<0.01 appealing des
Most
Time on task (s) * 190 (3.17)p<0.05 (2.82)
169 177 (2.95) p<0.01
In exploratory tasks, participants visited significantly more
5. DISCUSSI
Our study has p
pages in the original condition (F(51,2)=5.615, p<0.01),
research question
where a post-hoc TukeyHSD saw only one key difference
support searcher
between UIQ and UIC. This finding may indicate that
have a fixed form
participants were able to find more relevant pages earlier in
Dr Max L. Wilson but hard to find a
http://cs.nott.ac.uk/~mlw/
51. refinements in the faceted UIF condition (F(51,2)=6.245, The choices high
p<0.005). Again, a post-hoc TukeyHSD revealed enjoyed and pref
significant differences between UIF and the two despite believing
Exploratory tasks
alternatives (both p<0.05), but no difference between UIC
and UIQ. Together, these two sets of results indicate that
baseline conditio
timing data, indi
participants behaved very differently in the three baseline felt fast
conditions, for exploratory tasks, using significantly fewer favourably in any
queries (with UIC) and significantly more refinements.
Table 5: Freq
Table 3: log data for exploratory tasks (*=significant) three co
Mean (std) UIQ UIC UIF Frequency of ch
Queries (#) * 3.11 (1.49) 1.44 (0.51) 2.44 (1.29) p<0.0005 to correct
Quickest
Refinements (#) * 2.17 (0.86) 1.78 (0.65) 3.39 (2.23) p<0.005enjoyment du
Most
Page visits (#) * 2.55 (1.04) 1.61 (0.69) 2.11 (0.75) p<0.01 appealing des
Most
Time on task (s) * 190 (3.17) 169 (2.82)
p<0.05 177 (2.95) p<0.01
In exploratory tasks, participants visited significantly more
5. DISCUSSI
Our study has p
pages in the original condition (F(51,2)=5.615, p<0.01),
research question
where a post-hoc TukeyHSD saw only one key difference
support searcher
between UIQ and UIC. This finding may indicate that
have a fixed form
participants were able to find more relevant pages earlier in
Dr Max L. Wilson but hard to find a
http://cs.nott.ac.uk/~mlw/
52. refinements in the faceted UIF condition (F(51,2)=6.245, The choices high
p<0.005). Again, a post-hoc TukeyHSD revealed enjoyed and pref
significant differences between UIF and the two despite believing
Exploratory tasks
alternatives (both p<0.05), but no difference between UIC
and UIQ. Together, these two sets of results indicate that
baseline conditio
timing data, indi
participants behaved very differently in the three baseline felt fast
conditions, for exploratory tasks, using significantly fewer favourably in any
queries (with UIC) and significantly more refinements.
Table 5: Freq
Table 3: log data for exploratory tasks (*=significant) three co
Mean (std) UIQ UIC UIF Frequency of ch
Queries (#) * 3.11 (1.49) 1.44 (0.51) 2.44 (1.29) p<0.0005 to correct
Quickest
Refinements (#) * 2.17 (0.86) 1.78 (0.65) 3.39 (2.23) p<0.005enjoyment du
Most
Page visits (#) * 2.55 (1.04) p<0.005(0.69)
1.61 p<0.05 (0.75)
2.11 p<0.01 appealing des
Most
Time on task (s) * 190 (3.17) 169 (2.82) 177 (2.95) p<0.01
p<0.05
In exploratory tasks, participants visited significantly more
5. DISCUSSI
Our study has p
pages in the original condition (F(51,2)=5.615, p<0.01),
research question
where a post-hoc TukeyHSD saw only one key difference
support searcher
between UIQ and UIC. This finding may indicate that
have a fixed form
participants were able to find more relevant pages earlier in
Dr Max L. Wilson but hard to find a
http://cs.nott.ac.uk/~mlw/
53. refinements in the faceted UIF condition (F(51,2)=6.245, The choices high
p<0.005). Again, a post-hoc TukeyHSD revealed enjoyed and pref
significant differences between UIF and the two despite believing
Exploratory tasks
alternatives (both p<0.05), but no difference between UIC
and UIQ. Together, these two sets of results indicate that
baseline conditio
timing data, indi
participants behaved very differently in the three baseline felt fast
conditions, for exploratory tasks, using significantly fewer favourably in any
queries (with UIC) and significantly more refinements.
Table 5: Freq
Table 3: log data for exploratory tasks (*=significant) three co
Mean (std) UIQ UIC UIF Frequency of ch
Queries (#) * 3.11 (1.49) 1.44 (0.51) 2.44 (1.29) p<0.0005 to correct
Quickest
Refinements (#) * 2.17 (0.86) 1.78 (0.65) 3.39 (2.23) p<0.005enjoyment du
Most
Page visits (#) * 2.55 (1.04) 1.61 (0.69) 2.11 (0.75) p<0.01 appealing des
Most
Time on task (s) * 190 (3.17) 169 (2.82) 177 (2.95) p<0.01
5. DISCUSSI
In UIC - fewer queries, fewer refinements, less page visits, and faster
- exploratory tasks, participants visited significantly more Our study has p
pages in the original condition (F(51,2)=5.615, p<0.01),
research question
where a more use of refinements (overall) and faster than UIQ
- UIF - post-hoc TukeyHSD saw only one key difference
support searcher
between UIQ and UIC. This finding may indicate that
have a fixed form
participants were able to find more relevant pages earlier in
Dr Max L. Wilson but hard to find a
http://cs.nott.ac.uk/~mlw/
54. 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
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
55. Motivation
Related Work
Information vs Interaction
Design Results Discussion
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
56. Hypotheses Revisted
H1:"Searchers"will"be"more"efficient"with"more"
powerful"interaction,"using"the"same"
metadata,"when"completing"search"tasks.
• In Simple tasks - participants were faster with UIC
• In Exp tasks
- participants were better in all 4 measures with either UIC.
- UIF was faster than UIQ, making more use of refinements
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
57. Hypotheses Revisted
H2:"Searchers"will"enjoy"more"powerful"
interaction,"despite"using"the"same"
metadata.
• UIC was preferred and given high satisfaction/ease of use
ratings
• UIF - however - was not.
- Participants were split in opinion
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
58. Hypotheses Revisted
H3:"Searchers"will"use"query"recommendations"more"
when"they"are"presented"differently."
• Yes - For Exploratory tasks only
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
59. Limitations
• UIF - was not perfect
• Auto-suggest - was not considered
• Measurements - were limited
- not easy to say if differences meant ‘better’
- we had no sense of relevance
- could benefit from TREC style measurements
• Num Participants - was not big
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
60. Conclusions
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?
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
61. Future Work
Query data
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
62. Future Work
Clustered Faceted
Query data
algorithms metadata
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/
63. Future Work
Facets
Clusters
Performance
Suggestions
(hypothetically)
Dr Max L. Wilson http://cs.nott.ac.uk/~mlw/