model-based research in human-computer interaction (hci): keynote at mensch und computer 2010

Image from: http://www.flickr.com/photos/ourcommon/480538715/

Ed H. Chi Principal Scientist and Area Manager

Augmented Social Cognition Area Palo Alto Research Center

@edchi [email protected]

1 2010-09-13 Mensch und Computer 2010 Keynote

  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

2010-09-13 4 Mensch und Computer 2010 Keynote

chartered to create the architecture of information & the office of the future

-‐  invented distributed personal computing

-‐  established Xerox’s laser printing business

-‐  created the foundation for the digital revolution

Graphical User Interface

Laser Printing

Ethernet

Bit-mapped Displays

Distributed File Systems

Page Description Languages

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.


UIST 2004 8

  We know that people can Block out the irrelevant content quite easily

  Until it’s semantically meaningful or important to you Hey,

Jurgen!

  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 Mensch und Computer 2010 Keynote 2010-09-13 9

Characteriza*on Models

Prototypes Evalua*ons


Start with Capturing User Traces

Mensch und Computer 2010 Keynote 11

  Scan   Skim   Decide   Action

2010-09-13


  human-‐information interaction is adaptive to the extent:

Net Knowledge Gained

Costs of Interaction MAXIMIZE [ ]


Start users at page with some goal

Flow users through the

network

Examine user patterns

Scent Values: Probabilities of

Transition

2010-09-13


  A store that knows your goal.   Over 50% reduction in task time.

2010-09-13


  Identify tasty pages   Waft scent backward along links

–  Loses intensity as it travels

remote diagnostics

copiers

fax machines

other maintenance

. . .

XC4411 XC5001

XC4411 copier

features Features:

remote diagnostics

. . .

digital copiers color copiers

back

2010-09-13 18

Partial information goal: “remote diagnostic technology”

62 copies/min.

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

Mensch und Computer 2010 Keynote


Associated Entries underlined in red

20

User first type search keywords: “anthrax symptoms”

Conceptually highlight any relevant passages and keywords

Draw user attention

2010-09-13 Mensch und Computer 2010 Keynote


(times capped at five minutes)

10/12 subjects preferred ScentTrails

2010-09-13

2005-10-21 UMN talk

25

  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

2010-09-13 Mensch und Computer 2010 Keynote

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

€

N(t) = N0 ⋅ ert

€

dNdt

= r⋅ N

Growth rate of population

Current population

Growth rate depends on: N = current population r = growth rate of the 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 –  Less-‐crowded niches / produce many

offspring

  K-‐Strategist –  Conservation –  Strong competitors in crowded niches /

invest more heavily in fewer offspring €

dNdt

= rN(1− NK)

[Gunderson & Holling 2001]


  Ecological population growth model –  Also depend on environmental conditions –  K, carrying capacity (due to resource limitation)

€

dNdt

= rN(1− NK)


  Follows a logistic growth curve

New Article


  Carrying Capacity as a function of time.


Topics Concepts

Users Documents

Tags

T1…Tn Encoding Decoding

Noise



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.


2010-09-13 48

Guide

Web

Howto

Tips Help

Tools

Tip

Tricks

Tutorial

Tutorials

Reference

Semantic Similarity Graph

Mensch und Computer 2010 Keynote

  Spreading Activation in a bi-‐graph   Computation over a very large data set

–  150 Million+ bookmarks

Tags URLs

P(URL|Tag)

P(Tag|URL)



Dellarocas, MIT Sloan Management Review

(1) Generate new tools and systems, new techniques (2) Generate data that looks like real behavioral data


“evidence file”

SENSEMAKING

process

search FORAGING

Bef

ore

Sear

ch externally-motivated

searchers

31%

framing the context

refining the requirements

FORMULATE REPRESENTATION

GATHER REQUIREMENTS

69%

13% 59% 28%

transactional

self-motivated searchers

navigational informational

Dur

ing

Sear

ch

Aft

er S

earc

h

step A

step B

TRANSACTION

step A

step B

DO NOTHING

search product /end product

ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

“evidence file”

SENSEMAKING

process

search FORAGING

externally-motivated searchers

31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

43% users engaged in pre-search social interactions.

150 reports of unique search experiences mapped to a canonical model of social search.

59% users engaged in post-search sharing.

Bef

ore

Sear

ch

Dur

ing

Sear

ch

Aft

er S

earc

h

3 types of search: informational search provides a compelling case for social search support.

reasons for interacting: thought others might be interested, to get feedback, out of obligation

reasons for interacting: to get advice, guidelines, feedback, or search tips

“evidence file”

SENSEMAKING

process

search FORAGING


31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

•  instant messaging (IM) to personal social connections near the search box

Bef

ore

Sear

ch

Dur

ing

Sear

ch

Aft

er S

earc

h

•  tag clouds from domain experts •  other users’ search trails (for feedback) •  related search terms (for feedback)

Similar to: Glance; Smyth"

•  sharing tools built-in to (search) site •  collective tag clouds (for feedback)

Spartag.us"

Mr. Taggy"

  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


UIST 2004 60

“Fitts-digraph energy”

€

t =Pij

IPLog2

Dij

Wi+1

⎛

⎝ ⎜

⎞

⎠ ⎟

⎡

⎣ ⎢

⎤

⎦ ⎥

j=1

27

∑i=1

27

∑

Human Movement Study: Fitts’ law

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

0 2000 4000 6000 8000

10000 12000 14000 16000 18000

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

English Letter Corpus (News, chat etc)

€

W A→B( ) = e−ΔE

kT if ΔE >0=1 if ΔE ≤ 0

Metropolis “random walk” optimization Alphabetical tuning

Word connectivity

[Zhai et al., 2000, 2002]

Slide adopted from Mary Czerwinski Keynote UIST 2004

Between just getting things done vs. finding out the science


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

A B

  Design, Prototype, Learn;   Then Re-‐design, Prototype, Learn   Sometimes that’s all you can do.





  If you can, you should codify your findings so that others can replicate it, learn from it, predict behavior from it.

  The basis of a true scientific field

  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 [email protected]

WikiDashboard.com MrTaggy.com Zerozero88.com

  Appropriate for the occasion


Poor heuristic

Good heuristic


Solo

Cooperative (“good hints”)


•  Synonyms •  Misspellings •  Morphologies

People use different tag words to express similar concepts.

Social Tagging Creates Noise


Web Server

Search Results

UI Frontend

• Delicious • Ma.gnolia • Other social cues

Crawling

• Tuples of bookmarks

• [User, URL, Tags, Time]

Database • P(URL|Tag) • P(Tag|URL) • Bayesian Network Inference

MapReduce

• Pre-computed patterns in a fast index

Lucene • Serve up search results

• Well defined APIs

Web Server

•  MapReduce: months of computa*on to a single day

•  Development of novel scoring func*on


“evidence file”

SENSEMAKING

process

search FORAGING


31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



Dur

ing

Sear

ch

Aft

er S

earc

h

step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

Bef

ore

Sear

ch

“evidence file”

SENSEMAKING

process

search FORAGING

Bef

ore

Sear

ch externally-motivated

searchers

31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



Dur

ing

Sear

ch

Aft

er S

earc

h

step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

  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

model-based research in human-computer interaction (hci): keynote at mensch und computer 2010

Technology

gro mensch und computer

user attention2010

relevant user

capturing user traces

goal flow users

partial information

slowinggrowthof wikipedia

users atprobabilities