Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010

Ed
H.
Chi

Principal
Scientist
and
Area
Manager

Augmented
Social
Cognition
Area

Palo
Alto
Research
Center

@edchi

echi@parc.com

2010-09-13 Mensch und Computer 2010 Keynote
1
Image from: http://www.flickr.com/photos/ourcommon/480538715/

  Early
fundamental
contributions
from:

–  Computer
scientists
interested
in
changing

how
we
interact
with
information

–  Psychologists
interested
in
the
implications

of
these
changes

  The
need
to
establish
HCI
as
a
science

–  Adopt
methods
from
psychology

–  Dual
purpose:
understand
nature
of
human

behavior
and
build
up
a
science
of
HCI

techniques.

9/13/10 HCIC "Living Lab" 2

2010-09-13 Mensch und Computer 2010 Keynote 3

  Problem:

–  Intellectual
over-‐specialization

  The
Memex

  Extend
the
powers
of
the
human
mind

with
technology

–  Individuals
could
attend
to
greater
spans

–  Facile
command
of
all
recorded
knowledge

–  Sharing
of
knowledge
gained


Graphical User Interface
chartered
to
create
the
architecture
of
Laser Printing
information
&
the
oﬃce
of
the
future
Ethernet

invented
distributed
personal
computing

-‐  Bit-mapped Displays

established
Xerox’s
laser
printing
business

-‐  Distributed File Systems
Page Description Languages

created
the
foundation
for
the
digital
revolution

-‐ 
First Commercial Mouse
Object-oriented Programming
WYSIWYG Editing
Distributed Computing
VLSI Design Methodologies
Optical Storage
Client/Server Architecture
Device Independent Imaging
Cedar Programming Language


  Fitts’
Law

  Models
of
Human
Memory

  Models
of
Human
Attention

  Interruptability

  Cognitive
and
Behavorial
Modeling

  Perception
and
Navigation

  …


  We
know
motion
in
the
periphery
is
more
noticeable

than
in
the
foveal
region
[DaVinci].

  Now
think
about
research
and
products
that
involve

animations
or
ﬂashing
icons.


  We
know
that
people

can
Block
out
the

irrelevant
content

quite
easily

  Until
it’s
semantically

meaningful
or

important
to
you
Hey,
Jurgen!

UIST 2004 8

Characteriza*on
Models

Evalua*ons
Prototypes

  Characterize
activity
with
experiments,
ethnography,
log
analysis

  Model
interaction
dynamics
and
interface
variations

  Prototype
tools
to
increase
beneﬁts
or
reduce
cost

  Evaluate
prototypes
with
users

9

Start with Capturing User Traces


  Scan

  Skim

  Decide

  Action


Characteriza*on
Models

Evalua*ons
Prototypes

  Characterize
activity
with
experiments,
ethnography,
log
analysis

  Model
interaction
dynamics
and
interface
variations

  Prototype
tools
to
increase
beneﬁts
or
reduce
cost

  Evaluate
prototypes
with
users

12

  human-‐information
interaction
is
adaptive
to
the
extent:

MAXIMIZE
[ Net Knowledge Gained
Costs of Interaction ]


Scent Values:
Start users at Probabilities of
page with Transition Examine user patterns
some goal
Flow users
through the
network


Characteriza*on
Models

Evalua*ons
Prototypes

  Characterize
activity
with
experiments,
ethnography,
log
analysis

  Model
interaction
dynamics
and
interface
variations

  Prototype
tools
to
increase
beneﬁts
or
reduce
cost

  Evaluate
prototypes
with
users

15

  A
store
that
knows
your
goal.

  Over
50%
reduction
in
task
time.


  Identify
tasty
pages

  Waft
scent
backward
along
links

–  Loses
intensity
as
it
travels

XC4411 copier
Features:
XC4411 features
digital copiers XC5001 remote diagnostics
color copiers
copiers ...
back
fax machines
other maintenance remote
diagnostics
...


Partial information goal: 62 copies/min.
“remote diagnostic
technology”

Remainder of
information goal: 92 copies/min.
“speed >= 75”


Associated Entries
underlined in red


Conceptually highlight any relevant
User ﬁrst type search keywords: passages and keywords

“anthrax symptoms”

Draw user attention


Characteriza*on
Models

Evalua*ons
Prototypes

  Characterize
activity
with
experiments,
ethnography,
log
analysis

  Model
interaction
dynamics
and
interface
variations

  Prototype
tools
to
increase
beneﬁts
or
reduce
cost

  Evaluate
prototypes
with
users

21

(times capped at five minutes)

10/12 subjects preferred ScentTrails


  Descriptive:
clarify
terms,
key
concepts

  Explanatory:
reveal
relationships
and
processes

  Predictive:
about
performance
and
situations

  Prescriptive:
convey
guidance
for
decision

making
in
design
by
recording
best
practice

  Generative:
enable
practitioners
to
create,

invent
or
discover
something
new


Bongwon
Suh,
Gregorio
Convertino,
Ed
H.
Chi,
Peter

Pirolli.
The
Singularity
is
Not
Near:
Slowing
Growth
of

Wikipedia.
In
Proc.
of
WikiSym
2009.
Oct,
2009.
Florida,

USA


Number of Articles (Log Scale)

http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth


Monthly Edits


*In thousands Monthly Active Editors


Monthly Ratio of Reverted Edits


  Preferential
Attachment:
Edits
beget
edits

–  more
number
of
previous
edits,
more
number
of
new
edits

Growth rate depends on:
N = current population
r = growth rate of the population

N(t) = N 0 ⋅ e rt
dN
= r⋅ N
dt
Growth rate Current
of population €
population

€

  Biological
system

–  Competition
increases
as

population
hit
the
limits
of
the

ecology

–  Advantage
go
to
members
of
the

population
that
have
competitive

dominance
over
others

  Analogy

–  Limited
opportunities
to
make

novel
contributions

–  Increased
patterns
of
conﬂict
and

dominance


  r-‐Strategist

–  Growth
or
exploitation

dN N
–  Less-‐crowded
niches
/
produce
many
= rN(1− )
oﬀspring
dt K
  K-‐Strategist

–  Conservation

[Gunderson & Holling 2001]
–  Strong
competitors
in
crowded
niches
/

invest
more
heavily
in
fewer
oﬀspring

€


  Ecological
population
growth
model

–  Also
depend
on
environmental
conditions

–  K,
carrying
capacity
(due
to
resource
limitation)

dN N
= rN(1− )
dt K

€

  Follows
a
logistic
growth
curve

New Article


  Carrying
Capacity
as
a
function
of
time.


Concepts
Topics

Users
Documents

Noise

Tags

Decoding
Encoding

T1…Tn


Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)


Joint
work
with

Rowan
Nairn,
Lawrence
Lee

Kammerer,
Y.,
Nairn,
R.,
Pirolli,
P.,
and
Chi,
E.
H.
2009.
Signpost
from
the

masses:
learning
eﬀects
in
an
exploratory
social
tag
search
browser.
In

Proceedings
of
the
27th
international
Conference
on
Human
Factors
in

Computing
Systems
(Boston,
MA,
USA,
April
04
-‐
09,
2009).
CHI
'09.
ACM,
New

York,
NY,
625-‐634.


Semantic Similarity Graph
Web
Tools
Reference

Guide
Howto

Tutorial
Tips
Help

Tip Tutorials

Tricks


Tags URLs

P(URL|Tag)

P(Tag|URL)

  Spreading
Activation
in
a
bi-‐graph

  Computation
over
a
very
large
data
set

–  150
Million+
bookmarks


Dellarocas,
MIT
Sloan
Management
Review


(1)
Generate
new
tools
and
systems,
new
techniques

(2)
Generate
data
that
looks
like
real
behavioral
data


externally-motivated self-motivated framing
the context

Before Search
searchers searchers

31% 69%
Social Interactions

GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION

28% 13% 59%
During Search

navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING

search product /end product
After Search

28% 72%
DO NOTHING TAKE ACTION

ORGANIZE DISTRIBUTE

to self 15% to proximate 87% to public 2%
others others

the context

Before Search
searchers searchers

31% 69%
43% users engaged in pre-search social Social Interactions
interactions.
the
reasons for interacting: to get advice, guidelines, feedback,
requirements
or search tips
28% 13% 59%
During Search

FORAGING
3 types of search: informational search provides a
150 reports of unique search experiences
compelling caseBfor social search support.
mapped to a canonical model of social search.
step B step
process
“evidence file”

After Search

28% 72%
59% users engaged in post-search sharing.
ORGANIZE DISTRIBUTE
reasons for interacting: thought others might be interested,
to get feedback, out of obligation
others others

the context

Before Search
searchers searchers

•  instant 31%
messaging69% to personal social
(IM) Social Interactions
connections near the search box
refining
GATHER REQUIREMENTS
the
requirements

28% 13% 59%
During Search

•  step A clouds from domain FORAGING
tag step A experts
search
•  step B users’ search trails process feedback)
other (for
step B
•  related search terms (for feedback) Similar to: Glance; Smyth"
“evidence file”

After Search

28% 72%
•  sharing tools built-in to (search) site Spartag.us"

•  collective tag clouds (for feedback)
ORGANIZE DISTRIBUTE
Mr. Taggy"

others others

  All
models
are
wrong!

–  Some
are
more
wrong
than
others!

  So
what
are
theories
and
models
good
for?

  They’re
a
summary
of
what
we
think
is
happening

–  Ways
to
describe
and
explain
what
we
have
learned

–  Predicts
user
and
group
behavior

–  Helps
generate
new
novel
tools
and
systems


Word connectivity
Human Movement Study: Fitts’ law

MT = a + b Log2(Dsi/Wi + 1)

18000

English Letter Corpus
16000

14000

12000

10000
(News, chat etc)
8000

6000

4000
[Zhai et al., 2000, 2002]
2000

0
sp E T A H O N S R I D L U W M C G Y F B P K V J X Q Z
Slide adopted from
Mary Czerwinski Keynote
UIST 2004

“Fitts-digraph energy”
27 27
Pij ⎡ ⎛ Dij ⎞ ⎤
t = ∑ ∑ ⎢ Log2 ⎜ +1⎟ ⎥ W ( A →B) = e
−ΔE
kT
if ΔE >0
i=1 j =1 IP ⎣
⎝ Wi ⎠ ⎦
=1 if ΔE ≤ 0
Metropolis “random walk”
optimization
Alphabetical tuning

UIST 2004 60
€ €

Between
just
getting
things
done

vs.
ﬁnding
out
the
science


A B
Bucket Testing or A/B Testing [Kohavi et al]

Characteriza*on
Models

Evalua*ons

Prototypes

Evalua*ons
Prototypes

  Design,
Prototype,
Learn;

  If
you
can,
you
should
codify
your

findings
so
that
others
can

  Then
Re-‐design,
Prototype,
Learn

replicate
it,
learn
from
it,
predict

  Sometimes
that’s
all
you
can
do.
behavior
from
it.

  The
basis
of
a
true
scientific
field

63

  Research
Vision:
Understand
how
social
computing

systems
can
enhance
the
ability
of
a
group
of

people
to
remember,
think,
and
reason.

http://asc-‐parc.blogspot.com

http://www.edchi.net

echi@parc.com

WikiDashboard.com
MrTaggy.com
Zerozero88.com

  Appropriate
for

the
occasion


Poor heuristic

Good heuristic


Solo

Cooperative (“good hints”)


Social Tagging Creates Noise

•  Synonyms
•  Misspellings
•  Morphologies

People use different tag
words to express similar
concepts.


Database Lucene
• Delicious • P(URL|Tag) • Serve up search
• Ma.gnolia • P(Tag|URL) results
• Tuples of • Pre-computed
• Other social cues bookmarks • Bayesian Network patterns in a fast • Well defined APIs
• [User, URL, Tags, Inference index
Time]
Crawling MapReduce Web Server
Web
Server

UI Search
Frontend Results
•  MapReduce:
months
of
computa*on
to
a
single
day

•  Development
of
novel
scoring
func*on


framing

Before Search
externally-motivated self-motivated
searchers searchers the context

31% 69%
Social Interactions

the
requirements

28% 13% 59%
During Search

FORAGING
process
step B step B
“evidence file”

After Search

28% 72%

ORGANIZE DISTRIBUTE

others others

the context

Before Search
searchers searchers

31% 69%
Social Interactions

the
requirements

28% 13% 59%
During Search

FORAGING
process
step B step B
“evidence file”


28% 72%
After Search


ORGANIZE DISTRIBUTE

others others

  For
example,
for
information
diffusion,
it’s
theory
of

influentials
[Gladwell,
etc.]

–  reach
a
small
group
of
influential
people,
and
you’ll
reach

everyone
else

Figure From: Kleinberg, ICWSM2009


From: Sun et al, ICWSM2009


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