This document summarizes Ed Chi's keynote presentation on augmented social cognition at the Hypertext 2010 workshop. Chi discusses characterizing social systems using analytics, modeling social interactions and dynamics, prototyping tools to increase benefits or reduce costs, and evaluating prototypes with real users. He provides examples of models for information diffusion and Wikipedia growth. Chi also covers challenges in identifying relevant models from literature and techniques for addressing noise in social tagging like synonyms, misspellings and morphologies.
Presentation for a doctoral seminar at the Glasgow Caledonian University Glasgow, UK, March 25, 2010. The argument put forth is that open, distributed infrastructures are the way go for networked learning, particularly in the non-formal settings that are needed for professional development to thrive.
Introduction aux systèmes de recommandation : filtrage collaboratif, filtrage par le contenu, recommandation de livres et de lectures.
Présentation dans le cadre des journées ARS2017, Université de la Manouba (Tunis)
Contains some personal reflections on how my university has responded to opportunities and challenges presented by emerging technologies, and the fundamental issues to be dealt with
Presentation for a doctoral seminar at the Glasgow Caledonian University Glasgow, UK, March 25, 2010. The argument put forth is that open, distributed infrastructures are the way go for networked learning, particularly in the non-formal settings that are needed for professional development to thrive.
Introduction aux systèmes de recommandation : filtrage collaboratif, filtrage par le contenu, recommandation de livres et de lectures.
Présentation dans le cadre des journées ARS2017, Université de la Manouba (Tunis)
Contains some personal reflections on how my university has responded to opportunities and challenges presented by emerging technologies, and the fundamental issues to be dealt with
The presentation I held at #ocg12, based on the paper "The case for an open science in technology enhanced learning" by P. Kraker, D. Leony, W. Reinhardt, and G. Beham
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...Maryam Farooq
For more AI talks, visit: nyai.co
These slides are from NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catherine Havasi, which took place Tues, 12/18/19 at Kirkland & Ellis NYC.
[Speaker Bio] Dr. Catherine Havasi is a technology strategist, artificial intelligence researcher, and entrepreneur. In the late 90s, she co-founded the Common Sense Computing Initiative, or ConceptNet, the first crowd-sourced project for artificial intelligence and the largest open knowledge graph for language understanding. ConceptNet has played a role in thousands of AI projects and will be turning 20 next year. She has started several companies commercializing AI research, including Luminoso where she acts as Chief Strategy Officer. She is currently a visiting scientist at the MIT Media Lab where she works on computational creativity and previously directed the Digital Intuition group.
[Abstract] People who build everything from entertainment experiences to financial management face a dilemma: how can you scale what you’re building for broader consumption, yet maintain the personalization that makes it special? A fundamental tension exists between building something individualized, and scaling it to consumers such as visitors at a theme park, or gamers exploring the latest Zelda adventure. True disruption happens when we overcome the idea that one must sacrifice personalization to achieve mass production — like it has in advertising, recommendations, and web search.
Artificial Intelligence practitioners, especially in natural language understanding, dialogue, and cognitive modeling, face the same issue: how can we personalize our models for all audiences without relying on unscalable efforts such as writing specific rules, building dialogue trees, or designing knowledge graphs? Catherine Havasi believes we can remove this dichotomy and achieve “mass personalization.” In this session we’ll discuss how to understand domain text and build believable digital characters. We’ll talk about how adding a little common sense, cognitive architectures, and planning is making this all possible.
nyai.co
The presentation shows 5 main trends for e-learning - it is a starting point for discussions, slides can be re-used for workshops on trend identification and roadmapping
Slides used to support workshop at Association of Learning Technology Conference. ALT-C 2009.
These slides are released under Creative Commons Attribution Non-Commercial Share Alike to respect copyright of images used and acknowledged within the presentation.
A quick little upload that outlines why I'm doing a thesis in transmedia storytelling. I've just handed it in but I thought I would put this up for anyone who was interested.
Stanford Center for Biomedical Informatics Research, Colloquium, Stanford University, Palo Alto, CA, Jan 6th, 2010
Presenter: Markus Strohmaier, Graz University of Technology, Austria
Understanding new ways of sharing content for learning and researching.@cristobalcobo
This lecture explores how the expansion of the Internet and a variety of digital devices has influenced the way that information and knowledge is generated, consumed and distributed particularly in the scholar environment.
PhD presentation for the public defense of the dissertation entitled 'Bridging the gap between Open and User Innovation? Exploring the value of Living Labs as a means to structure user contribution and manage distributed innovation.' This was a joint PhD between Ghent University and the VUB.
Promotors:Prof. dr. Lieven De Marez, Universiteit Gent, Faculteit Politieke & Sociale Wetenschappen, vakgroep Communicatiewetenschappen and Prof. dr. Pieter Ballon, Vrije Universiteit Brussel, Faculteit Economische en Sociale Wetenschappen, vakgroep Communicatiewetenschappen
President of the jury:
Prof. dr. Gino Verleye, Universiteit Gent
Jury:
Prof. dr. Pieter Verdegem, Universiteit Gent
Prof. dr. Marcel Bogers, Associate Professorat Mads Clausen Institute, Faculty of Engineering, University of Southern Denmark
Prof. dr. Esteve Almirall, Profesor Asociado at ESADE Business & Law School
Prof. dr. Seppo Leminen, Principal lecturer at Laurea University of Applied Sciences & Adjunct Professor at Aalto University School of Economics
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
HCI Korea 2012 Keynote Talk on Model-Driven Research in Social ComputingEd Chi
Model-Driven Research in Social Computing
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.
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The presentation I held at #ocg12, based on the paper "The case for an open science in technology enhanced learning" by P. Kraker, D. Leony, W. Reinhardt, and G. Beham
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These slides are from NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catherine Havasi, which took place Tues, 12/18/19 at Kirkland & Ellis NYC.
[Speaker Bio] Dr. Catherine Havasi is a technology strategist, artificial intelligence researcher, and entrepreneur. In the late 90s, she co-founded the Common Sense Computing Initiative, or ConceptNet, the first crowd-sourced project for artificial intelligence and the largest open knowledge graph for language understanding. ConceptNet has played a role in thousands of AI projects and will be turning 20 next year. She has started several companies commercializing AI research, including Luminoso where she acts as Chief Strategy Officer. She is currently a visiting scientist at the MIT Media Lab where she works on computational creativity and previously directed the Digital Intuition group.
[Abstract] People who build everything from entertainment experiences to financial management face a dilemma: how can you scale what you’re building for broader consumption, yet maintain the personalization that makes it special? A fundamental tension exists between building something individualized, and scaling it to consumers such as visitors at a theme park, or gamers exploring the latest Zelda adventure. True disruption happens when we overcome the idea that one must sacrifice personalization to achieve mass production — like it has in advertising, recommendations, and web search.
Artificial Intelligence practitioners, especially in natural language understanding, dialogue, and cognitive modeling, face the same issue: how can we personalize our models for all audiences without relying on unscalable efforts such as writing specific rules, building dialogue trees, or designing knowledge graphs? Catherine Havasi believes we can remove this dichotomy and achieve “mass personalization.” In this session we’ll discuss how to understand domain text and build believable digital characters. We’ll talk about how adding a little common sense, cognitive architectures, and planning is making this all possible.
nyai.co
The presentation shows 5 main trends for e-learning - it is a starting point for discussions, slides can be re-used for workshops on trend identification and roadmapping
Slides used to support workshop at Association of Learning Technology Conference. ALT-C 2009.
These slides are released under Creative Commons Attribution Non-Commercial Share Alike to respect copyright of images used and acknowledged within the presentation.
A quick little upload that outlines why I'm doing a thesis in transmedia storytelling. I've just handed it in but I thought I would put this up for anyone who was interested.
Stanford Center for Biomedical Informatics Research, Colloquium, Stanford University, Palo Alto, CA, Jan 6th, 2010
Presenter: Markus Strohmaier, Graz University of Technology, Austria
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This lecture explores how the expansion of the Internet and a variety of digital devices has influenced the way that information and knowledge is generated, consumed and distributed particularly in the scholar environment.
PhD presentation for the public defense of the dissertation entitled 'Bridging the gap between Open and User Innovation? Exploring the value of Living Labs as a means to structure user contribution and manage distributed innovation.' This was a joint PhD between Ghent University and the VUB.
Promotors:Prof. dr. Lieven De Marez, Universiteit Gent, Faculteit Politieke & Sociale Wetenschappen, vakgroep Communicatiewetenschappen and Prof. dr. Pieter Ballon, Vrije Universiteit Brussel, Faculteit Economische en Sociale Wetenschappen, vakgroep Communicatiewetenschappen
President of the jury:
Prof. dr. Gino Verleye, Universiteit Gent
Jury:
Prof. dr. Pieter Verdegem, Universiteit Gent
Prof. dr. Marcel Bogers, Associate Professorat Mads Clausen Institute, Faculty of Engineering, University of Southern Denmark
Prof. dr. Esteve Almirall, Profesor Asociado at ESADE Business & Law School
Prof. dr. Seppo Leminen, Principal lecturer at Laurea University of Applied Sciences & Adjunct Professor at Aalto University School of Economics
Similar to Model-Driven Research in Social Computing (20)
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
HCI Korea 2012 Keynote Talk on Model-Driven Research in Social ComputingEd Chi
Model-Driven Research in Social Computing
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.
Location and Language in Social Media (Stanford Mobi Social Invited Talk)Ed Chi
http://forum.stanford.edu/events/2012mobi.php
Title: Location and Language in Social Media
Ed H. Chi
Staff Research Scientist, Google Research
(work done at [Xerox] PARC)
Abstract:
Despite the widespread adoption of social media internationally,
little research has investigated the differences among users of
different languages. Moreover, we know relatively little about how
people reveal their location information. In this talk, I will
outline our recent characterization studies on how users of differing
geographical locations and languages use social media.
First, on geographical location: We found that 34% of users did not
provide real location information in Twitter, frequently incorporating
fake locations or sarcastic comments that can fool traditional
geographic information tools. We performed a simple machine learning
experiment to determine whether we can identify a user’s location by
only looking at what that user tweets.
Second, on language, Examining users of the top 10 languages, we
discovered cross-language differences in adoption of features such as
URLs, hashtags, mentions, replies, and retweets.
We discuss our work’s implications for research on large-scale social
systems and design of cross-cultural communication tools.
Homepage:
edchi.net
Speaker Bio:
Ed H. Chi is a Staff Research Scientist at Google. Until recently, he
was the Area Manager and a Principal Scientist at Palo Alto Research
Center's Augmented Social Cognition Group. He led the group in
understanding how Web2.0 and Social Computing systems help groups of
people to remember, think and reason. Ed completed his three degrees
(B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and
has been doing research on user interface software systems since 1993.
He has been featured and quoted in the press, including the Economist,
Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 90 research articles, his most well-known
past project is the study of Information Scent --- understanding how
users navigate and understand the Web and information environments. He
also led a group of researchers at PARC to understand the underlying
mechanisms in online social systems such as Wikipedia and social
tagging sites. He has also worked on information visualization,
computational molecular biology, ubicomp, and recommendation/search
engines, and has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and
snowboarder.
Model-Driven Research in Social Computing
Abstract:
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building
Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
CSCL 2011 Keynote on Social Computing and eLearningEd Chi
Ed H. Chi
Google Research (Work done at Xerox PARC)
CSCL2011 Keynote Abstract:
Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
Indeed, increasingly, new social online resources such as social bookmarking sites and Wikis are becoming central in eLearning. By studying them, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for social computing and social learning.
-----
Large Scale Social Analytics on Wikipedia, Delicious, and Twitter (presented ...Ed Chi
Ed H. Chi, Palo Alto Research Center
Large-Scale Social Analytics in Wikipedia, Delicious, and Twitter
Abstract
We will illustrate an analytical research approach in social computing. Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. The drive to build models and theories for social computing research should further our understanding of how network science, behavioral economics, and evolutionary theories could explain how social systems work. Here we will summarize the published research we conducted on large-scale social analytics in Wikipedia, Delicious, and Twitter, and point out how social analytics can help us understand the intricacies of large social systems.
About the Speaker
Ed H. Chi is area manager and principal scientist at Palo Alto Research Center's Augmented Social Cognition Group. He leads the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, such as the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 70 research articles, he has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
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1. Ed
H.
Chi,
Principal
Scientist
and
Area
Manager
Augmented
Social
Cognition
Area
Palo
Alto
Research
Center
Hypertext 2010 Keynote at MSM
2010-06-13 Workshop
1
Image from: http://www.flickr.com/photos/ourcommon/480538715/
2. Cognition:
the
ability
to
remember,
think,
and
reason;
the
faculty
of
knowing.
Social
Cognition:
the
ability
of
a
group
to
remember,
think,
and
reason;
the
construction
of
knowledge
structures
by
a
group.
– (not
quite
the
same
as
in
the
branch
of
psychology
that
studies
the
cognitive
processes
involved
in
social
interaction,
though
included)
Augmented
Social
Cognition:
Supported
by
systems,
the
enhancement
of
the
ability
of
a
group
to
remember,
think,
and
reason;
the
system-‐supported
construction
of
knowledge
structures
by
a
group.
Citation:
Chi,
IEEE
Computer,
Sept
2008
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3. Characteriza*on
Models
Evalua*ons
Prototypes
Characterize
activity
on
social
systems
with
analytics
Model
interaction
social
and
community
dynamics
and
variables
Prototype
tools
to
increase
benefits
or
reduce
cost
Evaluate
prototypes
via
Living
Laboratories
with
real
users
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4. All
models
are
wrong!
– Some
are
more
wrong
than
others!
So
what
are
theories
and
models
good
for?
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
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5. 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
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6. From: Sun et al, ICWSM2009
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7. 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
7
8. A
tough
task
to
identify
models
from
the
literature,
since
it
is
so
spread
out
in
various
publications
Just
a
few
examples
from
our
group.
UIST 2004 8
19. 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
€
20. Ecological
population
growth
model
– Also
depend
on
environmental
conditions
– K,
carrying
capacity
(due
to
resource
limitation)
dN N
= rN(1− )
dt K
€
23. 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
24. 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
€
26. Social Tagging Creates Noise
• Synonyms
• Misspellings
• Morphologies
People use different tag
words to express similar
concepts.
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27. Encoding
Retrieval
“video
people
talks
technology”
h:p://www.ted.com/index.php/speakers
h:p://edge.org
“science
research
cogni*on”
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33. 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.
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34. Semantic Similarity Graph
Web
Tools
Reference
Guide
Howto
Tutorial
Tips
Help
Tip Tutorials
Tricks
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35. Tags URLs
P(URL|Tag)
P(Tag|URL)
Spreading
Activation
in
a
bi-‐graph
Computation
over
a
very
large
data
set
– 150
Million+
bookmarks
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36. 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
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40. Dellarocas, MIT Sloan Management Review
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41. (1)
Generate
new
tools
and
systems,
new
techniques
(2)
Generate
data
that
looks
like
real
behavioral
data
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42. Poor heuristic
Good heuristic
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43. Solo
Cooperative (“good hints”)
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44. Appropriate
for
the
occasion
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45. 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
46. framing
Before Search
externally-motivated self-motivated
searchers searchers the context
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
47. 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
48. 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
28% 72%
After Search
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
49. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
43% users engaged in pre-search social Social Interactions
interactions.
GATHER REQUIREMENTS refining
the
reasons for interacting: to get advice, guidelines, feedback,
FORMULATE REPRESENTATION
requirements
or search tips
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A search
step A
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”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
59% users engaged in post-search sharing.
ORGANIZE DISTRIBUTE
reasons for interacting: thought others might be interested,
to get feedback, out of obligation
to self 15% to proximate 87% to public 2%
others others
50. externally-motivated self-motivated framing
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
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
• 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”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
• sharing tools built-in to (search) site Spartag.us"
• collective tag clouds (for feedback)
ORGANIZE DISTRIBUTE
Mr. Taggy"
to self 15% to proximate 87% to public 2%
others others
52. Research
Vision:
Understand
how
social
computing
systems
can
enhance
the
ability
of
a
group
of
people
to
remember,
think,
and
reason.
Living
Laboratory:
Create
applications
that
harness
collective
intelligence
to
improve
knowledge
capture,
transfer,
and
discovery.
http://asc-‐parc.blogspot.com
http://www.edchi.net
echi@parc.com
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Image from: http://www.flickr.com/photos/ourcommon/480538715/