A presentation given in May 2013. Talk about related work developed at IRIS lab at the School of Information Sciences, University of Pittsburgh.

1.
Informa)on
Access
on
the
Social
Web
2013/5/20

2. Agenda
2
Collaborative Exploratory Search
Integrated People Search
Virtual Reference and Community-based QA
Closing Remarks
Social Information Access
Dual Perspective Image Finding

3. Informa-on
Access
— Information
Access:
an
interactive
process
starts
with
a
user
noticing
his/her
needs
and
ends
with
the
user
obtaining
the
necessary
information
— Iterative,
multiple
stages,
many
back
loops

4. User
Generated
Content

5. Social
Networks

6. Social
Informa-on
Access
— Social
Information
Access:
information
access
using
“community
wisdom”
— Distilled
from
the
actions
in
real/virtual
community
— Collaboration
in
explicit
or
implicit
manner
— Social
information
access
technologies
capitalize
on
the
natural
tendency
of
people
to
follow
direct
and
indirect
cues
of
others’
activities
— Going
to
a
restaurant
that
attract
many
customers
— Asking
others
what
movies
to
watch.

7. Space
of
Social
Informa-on
Access
— [Brusilovsky2012]’s
taxonomy
for
social
info
access
7

8. Space
of
Social
Informa-on
Access
— [Brusilovsky2012]’s
taxonomy
for
social
info
access
— However,
8

9. More
Social
Informa-on
Access
— Collaboration
can
be
explicit,
not
just
implicit
— Explicit
Collaboration:
users
work
as
a
team
to
complete
the
same
task
9
Implicit Collaboration Explicit Collaboration

10. More
Social
Informa-on
Access
— Target
can
be
people,
not
just
documents
— Documents
can
be
used
to
represent
people
— People
should
be
modeled
in
network,
not
just
by
themselves
— Relationship
is
as
important
as
the
documents
generated
by
the
people
10

11. More
Social
Informa-on
Access
— Content
can
be
user
generated,
not
just
expert
generated
— User
generated
content
is
noisy,
flat,
but
easy
to
scale
up
11
Expert Generated Content User Generated Content

12. More
Social
Informa-on
Access
— Can
social
information
access
learn
from
library
service,
or
vice
versa?
12

13. Explicit
Collaboration:
Collaborative
Exploratory
Search
Collaborate with Zhen Yue, Shuguang Han

14. Collabora-ve
Exploratory
Search
— Complex
information
needs
such
as
exploratory
search
may
lead
to
collaboration
— Students
working
on
a
class
project
— Friends
looking
for
information
to
plan
a
vacation
Understand group activitities
involved in the collaborative
exploratory search process
Accommodate and support
user activities in collaborative
exploratory search
Analyzing Collaborative Search
Process
Data analysis method
User
behavior
Designing Collaborative Search
System

15. CollabSearch
System
q Search functions
- Web Search
- Save/edit/rate/tag Web pages/snippets
- Space for search task description
15

16. CollabSearch
System
§ http://crystal.exp.sis.pitt.edu:8080/CollaborativeSearch/
16

17. Categorizing
User
Ac-ons
Actions
Descriptions
Query
(Q)
A
user
issues
a
query
or
clicks
on
a
query
from
search
history
View
(V)
A
user
clicks
on
a
result
in
the
returned
result
list
Save
(S)
A
user
saves
a
snippet
or
bookmarks
a
webpage
Workspace
(W)
A
user
clicks
on
or
edits
an
item
saved
in
the
workspace
Topic
(T)
A
user
clicks
on
the
topic
statement
Chat
(C)
A
user
sends
an
message
or
views
the
chat
history

18. Pre-­‐Query
Ac-ons
18
Collect
View
Workspace
Query
Chat
Topic
Collect
View
Workspace
Topic
Query
Collaborative Search
Individual search
v Possible
benefit
of
explicit
communication
in
collaborative
search
§ Helping
users
to
generate
queries.

19. Pre-­‐chat
and
post-­‐chat
analysis
19
Chat
View
Query
Collect
Workspace
Topic
View
Query
Save
Chat
Topic
Workspace
Reasons that trigger the chatting:
Needs for discussing task requirements and
item collected.
Post-chat:
Check workspace
Issuing a query
Check the topic statement

20. Dimensions
of
User
Interac-ons
20

21. Interac-ons
in
Collabora-ve
Search
Interactions Description
Search
–
query
–
self
(Q)
A
user
issues
a
query
Select-­‐
item-­‐self
(V)
A
user
clicks
on
a
result
in
the
returned
result
list
Capture-­‐item-­‐self
(S)
A
user
saves
a
snippet
or
bookmarks
a
webpage
Scan-­‐list
of
saved
item
–
mixed
(Wm)
A
user
checks
the
workspace
without
clicking
on
any
particular
item.
Select
–
single
saved
item
–self
(Ws)
A
user
clicks
on
an
item
in
the
workspace
saved
by
him/
herself
Select
–
single
saved
item
–
partner
(Wp)
A
user
clicks
on
an
item
in
the
workspace
saved
by
the
partner
Scan-­‐topic
-­‐shared
(T)
A
user
clicks
the
topic
statement
for
view
Communicate-­‐
messages-­‐self
(Cs)
A
user
sends
a
message
to
the
other
user
Communicate-­‐message-­‐partner
(Cp)
A
user
receives
a
message
from
the
other
user
21

22. Transi-on
analysis
using
HMM
— Disadvantage
of
previous
methods
— Missing
global
view
of
search
behaviors
— Hard
to
determine
the
segments
of
sequential
behaviors
as
different
search
states
— Model
search
states
as
hidden
variables
A Hidden Markov Model for Action transitions
22

23. Hidden
States
and
Transi-ons
Q V S Wm Ws Wp T Cs Cp
HQ 0.82 0.13
HV 0.87 0.1
HS 0.88
HD 0.36 0.36 0.21
HW 0.37 0.44
0.12
HC 0.44 0.47
23
Collaborative Search
Individual Search

24. What
We
Learned
— Collaborative
search
process
have
patterns
— More
collaboration-­‐oriented
actions
as
the
collaboration
level
increase
— Transitions
within
search-­‐oriented
actions
and
within
collaboration-­‐oriented
actions
are
more
frequent
than
between
them
in
all
three
conditions.
— Explicit
and
implicit
communication
has
potential
benefit
on
helping
using
generating
query
ideas.
24

25. People
Search
in
their
Networks:
PeopleExplorer
Collaborate with Shuguang Han, Zhen Yue

26. Search
for
People
— People
use
search
engines
in
daily
basis
— But
many
are
People
Search
— Find
appropriate
collaborators
— Find
conference
program
committee
members
— Find
qualified
job
candidates
— Find
appropriate
experts
to
answer
questions
in
online
QA
(Question
Answering)
system
26
query=“experts
in
information
retrieval”

27. — Unable
to
support
diverse
tasks
in
one
system
— Only
focus
on
one
type
of
people
search
task,
but
task
contexts
are
diverse
— Find
keynote
speakers:
authoritativeness
— Find
collaborators:
social
closeness
— Unable
to
support
personalizing
user
preferences
— Even
in
the
same
task,
users
have
different
preferences.
e.g.
finding
thesis
committee
members
— Some
users
prefer
to
find
domain
expert
— Some
prefer
to
find
someone
who
are
easily
to
be
connected
— Unable
to
support
exploratory
search
process
— Exploration
is
an
iterative
and
interactive
process.
Users
may
need
to
learn
the
importance
of
each
criterion
27
Limita-on
of
Exis-ng
People
Search

28. The
PeopleExplorer
System
— The
proposed
method
— Represent
task
diversity
through
multiple
facets
— Allow
users
personalize
the
importance
of
each
facet
— Explore
the
importance
of
each
facet
(system
explained
why
each
candidate
is
returned
in
candidate
surrogate)
— The
Dataset
— 151,165
ACM
hosted
conference
papers
— In
computer
science
and
information
science
fields
— From
2000
to
2011
— 209,592
unique
authors
— Title,
abstract
and
authors
of
each
paper
28

29. 29
query = “recommender system”
Users’ exploration on three facets
Candidate Surrogate
Workspace

30. — Content
Relevance
—
I:
Retrieve
a
set
of
relevant
documents
for
each
query
—
II:
Pass
the
score
from
document
to
each
of
its
authors
—
III:
Rank
author
based
on
its
integrated
score
— Title
and
Abstract
were
indexed
for
document
search
— Authoritativeness
— PageRank**
— Decomposed
a
coauthor
link
into
two
directional
links
Method
** Illustration of
Authoritativeness, from Wikipedia
30

31. — Social
Similarity
— Measured
by
#
common
coauthors
two
people
shared
— Users
can
also
build
their
social
profiles,
the
similarity
is
measured
by
the
aggregated
similarity
for
all
connections
in
your
social
profile
— Integration
— Log-­‐Linear
combination
with
weights
indicating
the
importance
of
each
facet
Method
31

32. Experiment
Design
— Exploratory
People
Search
Tasks
— Conference
Mentor
Finding
— Expectation:
Authoritativeness
is
important
— New
Coauthor
Finding
— Expectation:
More
social
similarity
— External
Thesis
Committee
Member
Finding
— Expectation:
both
social
similarity
and
authoritativeness
are
important
— Reviewer
Suggestion
— Expectation:
Less
social
similarity
— Two
Systems
— Experimental
system
and
baseline
system
32

33. 33
The Experimental system
The Baseline system

34. Example
Tasks
34

35. Par-cipants
— 24
participants
— 10
are
female,
14
are
male
— All
are
PhD
students
majoring
in
computer
science
and
information
science
from
8
Universities
— Research
interests
are
diverse:
information
retrieval,
computer
graphics,
GIS,
information
security,
health
informatics,
graphic
model
— 92%
of
them
searched
at
least
2-­‐3
times
a
month.
— 67%
of
them
searched
for
people
at
least
once
a
week
in
academic
search
engines
such
as
Google
Scholar
and
Microsoft
Academic
Search.
35

36. Result
Analysis
— System
Usage
Analysis
— How
did
people
use
two
systems?
— System
Performance
— Whether
the
experimental
system
is
better
in
terms
of
both
Efficiency
and
Effectiveness
?
— User
Perceptions
— How
did
users
perceived
the
performance
of
the
system?
— Task
Contexts
— The
importance
of
each
facet
in
different
tasks
and
among
different
users
36

37. System
Usage
— Number
of
unique
queries
(NUQ)
— Overall,
no
significant
difference,
but
has
significance
for
Conference
mentor
finding
task
(p=0.037)
— Number
of
result
pages
users
clicked
(NP)
— Experimental
system
is
significantly
better
— How
many
times
users
tuned
the
slide
bars
(NSB)
37

38. System
Effec-veness
— Average
rank
position
of
the
marked
candidates
(ARP)
— Average
relevance
score
over
the
five
selected
candidates
(ARel)
— Number
of
returned
candidates
(NC)
and
the
number
of
unique
candidates
(NUC)
generated
by
the
system
for
each
task.
38

39. System
Efficiency
— Overall,
No
significant
difference
has
been
found
} But significant (p = 0.1) for Task 1
39

40. System
Efficiency
— Overall,
No
significant
difference
has
been
found
} The time spent for finding the first candidate is significant for Task 2
40

41. User
Percep-ons
— Usability
questions
} Interaction
between
Task
and
Satisfactory
In
Q4
41

42. Task
Contexts
Analysis
— The
importance
of
each
facet
in
different
tasks
— Record
the
weights
for
each
facet
when
selecting
a
candidate,
— If
weight
of
the
facet
≠
0,
we
think
this
facet
is
important
— count
the
number
of
candidates
view
each
facet
as
important
42

43. Insights
— People
finding
tasks
do
need
iterative
and
interactive
system
support
— Users
only
need
to
check
fewer
unique
candidates
in
the
top
rank
positions.
— The
candidates
are
more
relevant.
Overall,
users
perceived
more
satisfied.
— Importance
of
each
facet
is
diverse
in
different
tasks
43

44. Combine
Expert
Content
with
User
Generated
Content
Collaborate with Yiling Lin and Peter Brusilovsky

45. Finding
Images
— Great
amount
of
images
created
daily
— Most
of
images
are
without
textual
content
45
Teenie
Harris
Arichive:
80,000
images
5
catalogers
who
worked
full
time
for
5
years

46. The
Flamenco
Search
Interface
46

47. 47

48. 48

49. — images
can
be
found
more
efficiently
and
effectively
when
more
than
one
information
indicators
are
provided
to
users
in
a
combined
manner
— Driven
by
information
scent
in
the
information
foraging
theory
49
Dual
Perspec-ve
Image
Finding

50. Dual-­‐Perspec-ve
Image
Finding
50
Provide
sufficiently
strong
information
scents
Allow
users
to
incrementally
reach
their
goal
Offer
efficient
and
informative
feedback

51. Informa-on
Flow
51

52. Research
Design
— “Teenie”
Harris
collection
at
Carnegie
Museum
of
Art
— 1,986
of
these
images
— 4,206
unique
tags
and
16,659
tag
assignments
using
Mturk
— Library
of
Congress
image
collection
in
Flickr.
— 12,541
images
— 39,737
unique
tags
and
1,216,318
tag
assignments
— provided
by
the
Library
of
Congress
and
Flickr’s
users
52

53. DPIF:
Flickr
LC
Collec-on
53

54. Baseline
1:
Subject
Heading
Only
54

55. Baseline
2:Tag
only
55

56. Research
Design
— Controlled
experiment
with
52
participants
from
great
Pittsburgh
area
— Data
will
be
recorded
with
multiple
methods:
— system
logs,
— a
pre-­‐test
(working
memory
capacity
test
&
background
survey),
— post-­‐questionnaire
after
each
task,
each
interface,
and
at
the
end
— a
structural
interview
— Search
tasks
— Lookup
tasks
— Exploratory
search
tasks
56

57. Search
Tasks
— Lookup
search
tasks
— 3
for
each
participant/system
— Total
9
lookups
— Exploratory
search
tasks
— 1
for
each
participant/system,
total
3
exploratory
tasks
57

58. Ini-al
Results
58

59. Learn
from
Current
and
Traditional:
Virtual
Reference
and
Community-­‐based
QA
Collaborate with Dan Wu at Wuhan University

60. Two
Social
Services
— Community-­‐based
Q&A
(cQA)
— Provide
knowledge
sharing
among
community
users
— Become
rapidly
developing
social
collaboration
platforms
— Build
participatory
platform
for
Q
&
A
among
community
users
— Collaborative
Digital
Reference
(cDR)
— Extend
reference
service
with
patrons
to
online
— Collaborate
among
libraries
with
different
expertise
&
working
schedules
— Learn
among
libraries
and
help
each
other
— Allocate
resources
better
according
to
users’
needs
— Build
collaborative
platform
for
Q
&
A
among
libraries
60

61. Research
Mo-va-ons
— cQA
and
cDR
are
two
instances
of
social
Q
&
A
— Both
enable
people
to
collaborate
in
answering
questions
— important
question:
the
differences
and
connections
between
cQA
and
cDR,
and
between
different
languages
— Research
Questions
— Q1:
through
the
set
of
questions
asked
at
the
selected
cQA
and
cDR
sites,
what
can
be
the
service
differences
in
term
of
answer
quality,
responsiveness
and
response
time?
— Q2:
Do
Chinese
sites
and
English
sites
reveal
differences
in
the
answers
to
Q1?
— Q3.
What
can
be
learned
from
cQA
to
improve
cDR?
61

62. Study
Design
— Sampling
method
— Aim
to
obtain
first-­‐hand,
focused
evaluation
— 2
languages:
English
and
Chinese
— 3
cDR
sites
and
3
cQA
sites
in
each
language
— 3X4
questions
and
domains
— 3
domains:
Economics,
literature,
library
science
— 4
types
of
questions:
Factual
questions,
enumerative
questions,
definition
questions
and
explorative
questions
— Answers:
obtained
from
encyclopedias,
Wikipedia
and
online
fact
books,
also
ask
domain
experts
62

63. Three
Chinese
cQA
Sites
— Baidu
Zhidao
63

64. Three
Chinese
cQA
Sites
— Sina
iAsk
64

65. Three
Chinese
cQA
Sites
— SOSO
Ask
65

66. Three
English
cQA
Sites
— Yahoo!
Answers
66

67. Three
English
cQA
Sites
— Answers.com
67

68. Three
English
cQA
Sites
— MadSci
Net
68

69. Three
Chinese
cDR
Sites
— Reference
Service
of
China’s
National
Science
Digital
Library
69

70. Three
Chinese
cDR
Sites
— Online
Joint
Knowledge
Navigation
70

71. Three
Chinese
cDR
Sites
— The
Collaborative
Reference
Network
71

72. Three
English
cDR
Sites
— QuestionPoint
72

73. Three
English
cDR
Sites
— IPL2
73

74. Three
English
cDR
Sites
— Ask
a
Librarian
74

75. 3X4
Ques-ons
and
Domains
Economics
Literature
Library
Science
Factual
questions
芒德尔•托宾效应最早是在哪篇文章中被
提出？
In
which
paper
was
the
idea
later
called
Mundell-­‐Tobin
effect
first
published?
迄今为止，诺贝尔文学奖已有多少位
获奖者？
How
many
people
have
won
the
Nobel
Prize
for
Literature
up
to
now?
世界图书首都评选是从哪一年
开始的？
From
which
year
did
the
selection
of
“World
Book
Capital”
begin?
Enumerative
questions
根据最新统计数据，中国有哪些企业进
入世界五百强前十名之列？
According
to
the
latest
data,
which
Chinese
corporations
are
among
the
top
ten
of
the
world’s
top
five
hundreds
enterprises?
在所有诺贝尔文学奖得主中，有哪些
人是从南美洲来的？
Among
all
the
Nobel
Literature
Prize
laureates,
who
are/were
from
South
America?
世界性的图书馆组织有哪些？
What
international
library
organizations
are
there?
Definition
questions
什么是流动性补偿？
What
does
compensation
for
liquidity
mean?
什么是泛文学？
What
does
pan-­‐literature
mean?
什么是iSchool？
What
is
iSchool?
Explorative
questions
全球经济复苏还需要多长时间？为什
么？
How
much
time
is
still
needed
for
global
economy
to
recover?
Why?
博客对大众文学有哪些影响？
What
impacts
have
the
blogs
made
on
the
popular
literature?
数字图书馆的快速发展会给实
体图书馆带来哪些方面的重大
变化？为什么会有这些变化？
What
important
changes
will
the
rapidly
developed
digital
libraries
bring
to
traditional
libraries?
And
why
are
there
these
changes?
75

76. Results:
Chinese
Sites
questions
cQA
sites
cDR
sites
Baidu
Zhidao
Sina
.iAsk
SOSO
Ask
The
Collaborative
Reference
Service
of
China’s
National
Science
Digital
Library
Online
Joint
Knowledge
Navigation
The
Collaborative
Reference
Network
of
Zhongshan
Library
at
Guangdong
Province
Factual
questions
Economics
0/0
0/0
0/0
0/1
1/1
0/0
Literature
0/1
1/1
1/1
1/1
1/1
1/1
Lib
Science
0/0
1/1
1/1
1/1
1/1
1/1
Enumerative
questions
Economics
1/1
1/2
2/2
1/1
1/1
0/0
Literature
0/0
1/1
2/2
0/0
1/1
1/1
Lib
Science
1/2
1/1
2/2
1/1
1/1
1/1
Definition
questions
Economics
1/1
1/1
2/2
1/1
1/1
1/1
Literature
1/2
1/2
1/2
0/0
1/1
1/1
Lib
Science
1/2
1/1
1/1
1/1
1/1
0/1
Explorative
questions
Economics
0/0
1/1
3/3
1/1
0/1
0/0
Literature
1/2
0/0
1/2
0/0
1/1
0/1
Lib
Science
1/2
0/0
1/1
0/1
0/1
0/0
76
43 answers for the 12
questions asked in cQA
Average 3.58 answers per
question
29 answers for the 12
questions asked in cDR
Average 2.42 answers per
question
33 answers are correct
(76.7%)
23 answers are correct
(79.3%)
Factual: 5 answers, 4 are
correct
Factual: 8 answers, 7 are
correct
Enumerative: 13 answers,
11 are correct
Enumerative: 7 answers, 7
are correct
Definition: 14 answers, 10
are correct
Definition: 8 answers, 7 are
correct
Explorative: 11 answers, 8
are correct
Explorative: 6 answers, 2
are correct

77. Results:
Chinese
Sites
rank
system/Q&A
websites
number
of
questions
that
received
answers
(out
of
12
questions)
number
of
correct
answers/
total
number
of
answers
correct
answer
rate
(%)
answering
time
(average
over
all
returned
answers)
1
SOSO
Ask
8
17/19
89.5
1
day,20
hours
and
3minutes
2
Online
Joint
Knowledge
Navigation
12
10/12
83.3
3
days
3
Sina.iAsk
8
9/11
80
13
days,19
hours
and
5
minutes
4
The
Collaborative
Reference
Service
of
China’s
National
Science
Digital
Library
9
7/9
77.7
7
days
5
The
Collaborative
Reference
Network
of
Zhongshan
Library
at
Guangdong
Province
8
6/8
75
8
hours
6
Baidu
Zhidao
8
7/13
53.8
6
days
and
15hours
77
SOSO Ask responded
relatively quickly and
produced the highest
number of answers
Online Joint
answered all 12
questions, and
responded very
quickly
Had the shortest response time, but the quality of the answers varies
cQA was not
faster at
providing
answers
when
comparing to
cDR

78. questions
cQA
sites
cDR
sites
Yahoo!
Answers
Library
of
Congress
IPL2
Factual
questions
Economics
0/0
1/1
1/1
Literature
1/1
1/1
1/1
Lib
Science
0/0
1/1
1/1
Enumerative
questions
Economics
1/1
0/0
1/1
Literature
1/2
0/0
1/1
Lib
Science
1/1
1/1
1/1
Definition
questions
Economics
1/2
0/0
1/1
Literature
0/1
0/0
1/1
Lib
Science
1/1
1/1
1/1
Explorative
questions
Economics
2/2
0/0
1/1
Literature
0/1
0/0
1/1
Lib
Science
2/3
0/1
1/1
Results:
English
Sites
78
15 answers for 10 of the
12 questions asked in
Yahoo! Answers
IPL provided 12 answers
to 12 questions
LC provided 6 answers to
6 of the 12 questions
10 answers are correct
(66.7%) in Yahoo! Answers IPL has 100% correct answers
LC has 83.3% correct answers
Factual: 1 answer, and is
correct
Factual: 6 answers, all are
correct
Enumerative: 4 answers, 3
are correct
Enumerative: 4 answers, 4
are correct
Definition: 4 answers, 2 are
correct
Definition: 4 answers, 4 are
correct
Explorative: 6 answers, 4
are correct
Explorative: 4 answers, 3
are correct

79. rank
system/Q&A
websites
number
of
questions
that
received
answers
(out
of
12
questions)
number
of
correct
answers/
total
number
of
answers
correct
answer
rate
(%)
answering
time
(average
over
all
returned
answers)
1
IPL2
12
12/12
100
14
days
2
Library
of
Congress
6
5/6
83.3
17
days
3
Yahoo!
Answers
10
10/15
66.7
2
days
4
MadSci
Net
1
0/1
0
/
5
Ask
a
librarian
1
0/0
0
/
6
Answers.com
0
0/0
0
/
Results:
English
Sites
79
IPL2 is the best online
service , 100% correct
answer rate, also
answers are all in high
quality
Yahoo! Answers
has the fastest
answering speed
and the largest
number of
answers. But its
answer quality is
lower than IPL2
and LC
Answers.com and Ask a Librarian did not answer our questions
LC only
answered half
of our
questions,
and took long
time to
answer

80. Between
Chinese
and
English
— Exhibit
many
similarities
— cQA
sites
are
good
at
enumerative
and
definition
question,
and
to
some
degree
explorative
questions,
but
poorly
on
factual
questions,
particularly
in
economics.
— cDR
sites
are
more
reliable,
and
produce
higher
quality
answers
even
though
number
of
answers
is
smaller
— Demonstrate
some
differences
— Screening
questions
differently:
our
questions
to
the
Chinese
sites
produced
more
responses,
whereas
two
English
sites
did
not
answer
our
questions
at
all.
— Response
time
is
shorter
in
Chinese
sites,
and
only
Yahoo!
Answers
is
in
comparable
response
timeframe.
Maybe
both
IPL2
and
Library
of
Congress
are
very
busy
80

81. What
We
Learned
— Pros
and
Cons
of
cQA
and
cDR
— cQA’s
advantages:
large
user
groups,
more
answers
returned.
— Consistent
with
Shachaf
(2009):
cQA
are
more
heavily
utilized
— cQA’s
Limitations:
information
of
different
qualities
and
the
shallowness
of
some
answers.
— cDR’s
advantages:
rich
and
reliable
reference
resources,
and
high
literacy
skills
of
reference
librarians.
— Consistent
with
Connaway
and
Radford
(2011):
information
quality
and
interpersonal
relationship
— Consistent
with
Shachaf
(2009):
librarians
are
valuable
for
answering
more
difficult
questions
— cDR’s
limitations:
slow
response
speed
and
smaller
numbers
of
answers.
81

82. What
We
Learned
— Inspirations
— How
to
speed
up
and
scale
up
cDR?
— make
the
cDR
reference
process
and
results
as
open
as
possible
— Lankes
(2004):
general
DR
model
contains
a
Q
&
A
archive
— Add
commenting,
tagging
and
discussing
functions
to
cDR
questions
and
answer
collections
— Build
up
more
feedback
and
participatory
mechanisms
— the
usages
of
cQA
answers
in
cDR
services
— ??An
answer
to
Connaway
and
Radford
(2011)
challenges:
“users
still
do
not
really
know
about
digital
reference
services”
— some
high
quality
cDR
services
make
them
available
in
well-­‐known
cQA
sites,
integrate
cDR
with
cQA
82

83. What
We
Learned
— Limitations
of
the
Study
— the
number
of
samples
is
small
— considering
the
popularity
of
cQA
sites
and
many
other
cDR
services
— Considering
the
wide
range
of
questions
asked
— our
selected
questions
and
our
native
language
might
trigger
or
prevent
some
responses
from
the
English
sites.
— it
would
be
better
to
have
a
survey
associated
with
the
questions
we
asked
so
that
some
reasons
behind
certain
reactions
from
the
sites
(such
as
lack
of
returned
answers
to
our
questions)
can
be
better
explained.
83

84. Closing
Remarks

85. Collabora-ve
Search
2.0
— Better
model
of
users
and
teams
— People
in
different
populations
— Teams
with
bigger
size
— Team
members
with
different
roles
— New
mobile
and
mixed
platform
— Smart
phones,
tablets,
laptops,
etc.
— Collaborative
search
process
or
systems
— Collaborative
search
are
more
popular
— But
collaborative
search
systems
are
not
widely
used

86. Heterogeneously
Social
— Heterogeneous
information
resources
— Articles,
web
pages,
blogs,
twitters,
facebooks,
youtube,
search
history
— Heterogeneous
platforms
— Communication
networks
— Interaction
platforms:
mobiles,
tablets,
laptops,
desktops
etc

87. Integra-on
with
LIS
— Social
information
access
develops
many
new
technology
on
information
organization,
information
storage
and
retrieval
— Scalable
and
quick,
but
noisy
and
shallow
— How
such
knowledge
can
be
integrated
with
traditional
expert
generated
knowledge
— Clean
and
deep,
but
static
and
87

88. Privacy
and
Security
— Social
information
in
general
is
open
— But
people
still
are
concerned
with
their
privacy
— Particularly
when
information
can
be
easily
aggregated
— Social
information
belongs
to
the
sites
— But
it
is
part
of
the
people’s
identity
and
assets
— How
to
maintain,
preserve
and
safe-­‐guard
social
information?
88

89. Access
Increasingly
More
Social
— Know
the
boundary
of
Social
Information
Access
— How
to
identify
which
tasks
are
good
for
social
information
access?
— How
to
effectively
integrate
social
networking,
direct
messaging,
and
social
recommendations
with
current
search
facilities.

90. Related
Publica-ons
— Dan
Wu,
Daqing
He.
(2013).
A
study
on
Q&A
services
between
community-­‐based
question
answering
and
collaborative
digital
reference
in
two
languages.
iConference
2013
Proceedings
(pp.
326-­‐337).
doi:10.9776/13205.
— Han,
Shuaguang;
Yue,
Zhen;
He,
Daqing.
Automatic
Identifying
Search
Tactic
in
Individual
Information
Seeking:
A
Hidden
Markov
Model
Approach.
iConference
2013.
— Zhen
Yue,
Shuguang
Han,
Daqing
He,
A
Comparison
of
Action
Transitions
in
Individual
and
Collaborative
Exploratory
Web
Search.
The
eighth
Asia
information
retrieval
societies
conference,
2012
— Zhen
Yue,
Jiepu
Jiang,
Shuguang
Han,
Daqing
He.
2012.
Where
do
the
Query
Terms
Come
from?
An
Analysis
of
Query
Reformulation
in
Collaborative
Web
Search.
In
Proceedings
of
the
21st
International
Conference
on
Information
and
Knowledge
Management
(CIKM
'12):
2595-­‐2598.
— Shuguang
Han,
Daqing
He,
Zhen
Yue,
Jiepu
Jiang
and
Wei
Jeng.
IRIS-­‐IPS:
An
Interactive
People
Search
System
for
HCIR
Challenge.
2012
Human-­‐Computer
Information
Retrieval
Symposium
(HCIR
Challenge
2012),
Boston,
IBM
Research
— Zhen
Yue,
Shuguang
Han,
Jiepu
Jiang,
and
Daqing
He.
2012.
Search
tactics
as
means
of
examining
search
processes
in
collaborative
exploratory
web
search.
In
Proceedings
of
the
5th
Ph.D.
workshop
on
Information
and
knowledge
(PIKM
'12).
ACM,
New
York,
NY,
USA,
59-­‐66.
DOI=10.1145/2389686.2389699
90

91. 91
Really Tough Questions
Please!!!

92. Acknowledgement
— The
work
presented
here
were
conducted
by
faculty
and
students
in
Information
Retrieval,
Integration
and
Synthesis
Lab
at
School
of
Information
Sciences
— Other
people
participated
in
these
works
are
— Prof.
Peter
Brusilovsky,
Prof
Dan
Wu
etc.
— These
work
are
partially
supported
by
the
National
Science
Foundation