GESIS
-‐
Leibniz
Ins.tute
for
the
Social
Sciences
HypTrails:
A
Bayesian
Approach
for
Comparing
Hypotheses
about
Human
Trails
on
the
Web
Philipp
Singer,
Denis
Helic,
Andreas
Hotho
and
Markus
Strohmaier
www.philippsinger.info/hyptrails
Vannevar
Bush
2
15.05.15
HypTrails
-‐
Philipp
Singer
image courtesy of brucesterling on Flickr Bush, V. (1945). As we may think. The Atlantic Monthly, 176(1):101– 108. Bush, V. (1945). As we may think. The Atlantic Monthly, 176(1): 101– 108. “[The
human
brain]
operates
by
associa5on.
With
one
item
in
its
grasp,
it
snaps
instantly
to
the
next
that
is
suggested
by
the
associa5on
of
thoughts.”
Human
trails
on
the
Web
18.05.15
HypTrails
-‐
Philipp
Singer
4
image courtesy of user Mmxx on Wikipedia ?
?
?
?
?
What
are
the
mechanisms
producing
human
trails
on
the
Web?
Example:
Human
navigaRonal
trails
• Humans
prefer
to
navigate
…
– H1:
over
semanRcally
similar
websites
– H2:
via
self-‐loops
(e.g.,
refreshing)
– H3:
by
using
the
structural
link
network
– H4:
by
preferring
similar
categories
– H5:
by
uRlizing
structural
properRes
– H6:
by
informaRon
scent
[West
et
al.
IJCAI
2009],
[Singer
et
al.
IJSWIS
2013],
[West
&
Leskovec
WWW
2012],
[Chi
et
al.
CHI
2001]
18.05.15
HypTrails
-‐
Philipp
Singer
5
Example:
Human
navigaRonal
trails
• Humans
prefer
to
navigate
…
– H1:
over
semanRcally
similar
websites
– H2:
via
self-‐loops
(e.g.,
refreshing)
– H3:
by
using
the
structural
link
network
– H4:
by
preferring
similar
categories
– H5:
by
uRlizing
structural
properRes
– H6:
by
informaRon
scent
[West
et
al.
IJCAI
2009],
[Singer
et
al.
IJSWIS
2013],
[West
&
Leskovec
WWW
2012],
[Chi
et
al.
CHI
2001]
18.05.15
HypTrails
-‐
Philipp
Singer
6
What
is
the
relaRve
plausibility
of
these
hypotheses
given
data?
HypTrails
in
a
nutshell
• Goal:
Express
and
compare
hypotheses
about
human
trails
in
a
coherent
research
approach
• Method:
– First-‐order
Markov
chain
model
– Bayesian
inference
• Idea:
– Incorporate
hypotheses
as
priors
– URlize
sensiRvity
of
marginal
likelihood
on
the
prior
• Outcome:
ParRal
ordering
of
hypotheses
15.05.15
HypTrails
-‐
Philipp
Singer
7
Bayesian
model
comparison:
Marginal
likelihood
15.05.15
HypTrails
-‐
Philipp
Singer
17
Probability
of
data
given
hypothesis
Model
evidence
Parameters
are
marginalized
out
Probability
of
observing
data
given
parameters
and
hypothesis
Bayesian
model
comparison:
Marginal
likelihood
15.05.15
HypTrails
-‐
Philipp
Singer
18
Probability
of
data
given
hypothesis
Model
evidence
Parameters
are
marginalized
out
Probability
of
observing
data
given
parameters
and
hypothesis
Probability
of
parameters
before
observing
data
Bayesian
model
comparison:
Marginal
likelihood
15.05.15
HypTrails
-‐
Philipp
Singer
19
Probability
of
data
given
hypothesis
Model
evidence
Parameters
are
marginalized
out
Probability
of
observing
data
given
parameters
and
hypothesis
Probability
of
parameters
before
observing
data
Hypothesis
Structure
of
HypTrails
16.05.15
HypTrails
-‐
Philipp
Singer
20
MC
Model
Hypothesis
(H1)
Belief
in
parameters
Prior
(H1)
ElicitaRon
Data
(Trails)
Marginal
likelihood
(H1)
Influence
Influence
EliciRng
priors
from
hypotheses
about
human
trails
• AdapRon
of
(trial)
roulefe
method
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-‐
Philipp
Singer
26
#Chips
=
k
Strength
of
hypothesis
k
=
18
EliciRng
priors
from
hypotheses
about
human
trails
• AdapRon
of
(trial)
roulefe
method
16.05.15
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-‐
Philipp
Singer
27
#Chips
=
k
Strength
of
hypothesis
k
=
18
à
Dirichlet
hyperparameters
Summary
• Studying
mechanisms
producing
human
trails
• HypTrails:
A
coherent
approach
for
expressing
and
comparing
hypotheses
about
human
trails
• Can
be
applied
to
all
kinds
of
human
trails
• ImplementaRons:
www.philippsinger.info/hyptrails
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-‐
Philipp
Singer
37
References
1/2
• [West
et
al.
WWW
2015]
– Robert
West,
Ashwin
Paranjape,
and
Jure
Leskovec:
Mining
Missing
Hyperlinks
from
Human
NavigaRon
Traces:
A
Case
Study
of
Wikipedia.
24th
InternaRonal
World
Wide
Web
Conference
(WWW'15),
Florence,
Italy,
2015.
• [De
Choudhury
et
al.
HT
2010]
– De
Choudhury,
Munmun
and
Feldman,
Moran
and
Amer-‐Yahia,
Sihem
and
Golbandi,
Nadav
and
Lempel,
Ronny
and
Yu,
Cong:
AutomaRc
construcRon
of
travel
iRneraries
using
social
breadcrumbs.
21st
ACM
conference
on
Hypertext
and
hypermedia,
2010.
• [Bestavros
CIKM
1995]
– Bestavros,
Azer:
Using
speculaRon
to
reduce
server
load
and
service
Rme
on
the
WWW.”
4th
InternaRonal
conference
on
InformaRon
and
knowledge
management.
1995.
• [Perkowitz
IJCAI
1997]
– Perkowitz,
Mike,
and
Oren
Etzioni:
AdapRve
web
sites:
an
AI
challenge.
15th
internaRonal
joint
conference
on
ArRfical
intelligence.
1997.
• [West
et
al.
IJCAI
2009]
– West,
Robert,
Joelle
Pineau,
and
Doina
Precup.
"Wikispeedia:
An
Online
Game
for
Inferring
SemanRc
Distances
between
Concepts."
IJCAI.
2009.
15.05.15
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-‐
Philipp
Singer
39
References
2/2
• [Singer
et
al.
IJSWIS
2013]
– Philipp
Singer,
Thomas
Niebler,
Markus
Strohmaier
and
Andreas
Hotho,
CompuRng
SemanRc
Relatedness
from
Human
NavigaRonal
Paths:
A
Case
Study
on
Wikipedia,
InternaRonal
Journal
on
SemanRc
Web
and
InformaRon
Systems
(IJSWIS),
vol
9(4),
41-‐70,
2013
• [West
&
Leskovec
WWW
2012]
– Robert
West
and
Jure
Leskovec:
Human
Wayfinding
in
InformaRon
Networks
21st
InternaRonal
World
Wide
Web
Conference
(WWW'12),
pp.
619–628,
Lyon,
France,
2012.
• [Chi
et
al.
CHI
2001]
– Chi,
Ed
H.,
et
al.
"Using
informaRon
scent
to
model
user
informaRon
needs
and
acRons
and
the
Web."
Proceedings
of
the
SIGCHI
conference
on
Human
factors
in
compuRng
systems.
ACM,
2001.
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-‐
Philipp
Singer
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