Enhancing the Social Web through Augmented Social Cognition ResearchEd Chi
Keynote talk given at the International Conference on Asia-Pacific Digital Libraries (ICADL) 2008. December, 2008 in Bali, Indonesia ICADL 2008 link here
We are experiencing the new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by Wikipedia and del.icio.us, Web 2.0 environments are turning people into social information foragers and sharers. Users interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."
PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, sociology, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Web 2.0 systems like social tagging, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.
In this talk, we summarize recent PARC work and early findings on: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.
Presentation made for the purpose of an academic assignment. Summarizes the concept of Web2.0, discusses its issues, case studies and perspective on its utilization.
Building Killer Communities And Taking Confluence SocialAtlassian
What's with all the hype around enterprise social computing? And how can Confluence be used to support collaborative applications that are social? This session breaks through the hype around social computing, discusses the practical benefits of being people-oriented, and explores approaches to use Confluence in a social context.
Customer Speakers: Guy Fraser of Adaptavist, Ali Ouni of KAPIT, Peter Reiser of SUN Microsystems
Key Takeaways:
* New social capabilities in Confluence 3.0
* Primer on enterprise social computing
* Approaches to make Confluence deployments social
Enhancing the Social Web through Augmented Social Cognition ResearchEd Chi
Keynote talk given at the International Conference on Asia-Pacific Digital Libraries (ICADL) 2008. December, 2008 in Bali, Indonesia ICADL 2008 link here
We are experiencing the new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by Wikipedia and del.icio.us, Web 2.0 environments are turning people into social information foragers and sharers. Users interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."
PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, sociology, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Web 2.0 systems like social tagging, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.
In this talk, we summarize recent PARC work and early findings on: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.
Presentation made for the purpose of an academic assignment. Summarizes the concept of Web2.0, discusses its issues, case studies and perspective on its utilization.
Building Killer Communities And Taking Confluence SocialAtlassian
What's with all the hype around enterprise social computing? And how can Confluence be used to support collaborative applications that are social? This session breaks through the hype around social computing, discusses the practical benefits of being people-oriented, and explores approaches to use Confluence in a social context.
Customer Speakers: Guy Fraser of Adaptavist, Ali Ouni of KAPIT, Peter Reiser of SUN Microsystems
Key Takeaways:
* New social capabilities in Confluence 3.0
* Primer on enterprise social computing
* Approaches to make Confluence deployments social
SemSearch09 workshop at WWW2009, April 21th 2009- http://km.aifb.uni-karlsruhe.de/ws/semsearch09/ - Paper available at: http://km.aifb.uni-karlsruhe.de/ws/semsearch09/semse2009_25.pdf
How Confluence Plays Well with Others — from CRM to SharePointAtlassian
Confluence is often at the center of a number of different enterprise systems. This session discusses different integration scenarios with Confluence and common enterprise systems, like portals and SharePoint.
Customer Speakers: Peter Jones of Autodesk, Charles Hall of EADS Astrium
Partner Speaker: Rob Castaneda of Customware
Key Takeaways:
* Common Confluence integration scenarios and approaches
* Understanding Confluence's role alongside other collaboration tools
[Workshop] Analyzing Your Deliverables: Developing the Optimal Documentation ...Scott Abel
Presented by Nicki Bleiel at Documentation and Training LIfe Sciences, June 23-26, 2008 in Indianapolis.
Documentation deliverables have evolved beyond manuals and online help in recent years, and with the emergence of Web 2.0, things are changing faster than ever. Technical communicators have many more options to enhance the user experience, and developing many of them provide the opportunity to work with other departments to find a more holistic approach to content development and delivery. But there is no one-size-fits-all set of solutions. This workshop will review the types of analysis you need to do to determine which deliverables are right for your project, your customer, and your company.
Other factors that can’t be ignored, such as translation needs, staff/time constraints, file size limitations, corporate image and control, and proprietary concerns will also be discussed, including:
Analyzing the Product
* Intended audience; delivery method (desktop, web application, etc.); competitor offerings; software development methodology. The UI as part of the Help system. Product Management expectations.
Identifying User Wants and Needs
* Preferences and expectations for information; work environment; knowledge and experience levels.
Ascertaining Internal Needs and Opportunities
* Working with Training, Support, and Marketing to reduce duplication and provide the user with consistent, useful information.
* Finding ways to incorporate information from other departments to improve documentation.
Accessing Deliverable Options
* What is the optimum mix for the product?
* The traditional: online help, manuals, embedded help, job aids, forums, web sites, technical support knowledgebases.
* Emerging trends: wikis, blogs, RSS feeds, software demonstrations, podcasts, and other collaborative tools. They can supplement and/or enhance the traditional. Or, they may be a better fit for internal knowledge management or marketing use.
Optimizing the Library
* Single-sourcing; best practices for structuring information; continuous publishing
Analyzing Your Deliverables: Developing the Optimal Documentation LibraryScott Abel
Presented Nicki Bleiel at Documentation and Training Life Sciences, June 23-26, 2008 in Indianapolis.
Documentation deliverables have evolved beyond manuals and online help in recent years, and with the emergence of Web 2.0, things are changing faster than ever. Technical communicators have many more options to enhance the user experience, and developing many of them provide the opportunity to work with other departments to find a more holistic approach to content development and delivery. But there is no one-size-fits-all set of solutions. This workshop will review the types of analysis you need to do to determine which deliverables are right for your project, your customer, and your company. Product analysis, user expectations and needs, internal needs, deliverable options, and optimizing your library will all be discussed; as well as translation needs, staff/time constraints, file size limitations, corporate image and control, and proprietary concerns.
Evolution of Collaborative Content Management
Even as IT spending slides, IT departments are having to handle more content, more users, provide more productivity, as well as more compliance.
They have to do all this at less cost.
The enterprise hasn't kept up with advances in collaboration - most still uses a shared drive + MS Office + Email.
Most knowledge workers:
- can't find documents
- can't find the right version of documents
- find it easier to search for competitors' info than their own company's info
- have lots more noise than signal
This presentation talks about the collaborative enteprise. Essentially providign Facebook-like features for the Enterprise.
● The Cost of Poor Collaborative Content Management
● Best Practice – On the Web and in the Enterprise
● Alfresco
● Alfresco Share
● Standards Support
● Total Cost of Ownership
● More Information
Intranet 2.0 - Integrating Enterprise 2.0 into your corporate intranetJames Dellow
Enterprise 2.0 opportunities and challenges; The technology building blocks: Blogs, RSS,
tags, search and wikis; Implementation approaches: Nature or nurture? Pulling it all together and getting started.
This presentation was made as a workshop at Intranet '07 on 20th September, 2007 in Sydney, Australia. Note: This version of the presentation pack contains only key slides and omits additional reading materials provided.
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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SemSearch09 workshop at WWW2009, April 21th 2009- http://km.aifb.uni-karlsruhe.de/ws/semsearch09/ - Paper available at: http://km.aifb.uni-karlsruhe.de/ws/semsearch09/semse2009_25.pdf
How Confluence Plays Well with Others — from CRM to SharePointAtlassian
Confluence is often at the center of a number of different enterprise systems. This session discusses different integration scenarios with Confluence and common enterprise systems, like portals and SharePoint.
Customer Speakers: Peter Jones of Autodesk, Charles Hall of EADS Astrium
Partner Speaker: Rob Castaneda of Customware
Key Takeaways:
* Common Confluence integration scenarios and approaches
* Understanding Confluence's role alongside other collaboration tools
[Workshop] Analyzing Your Deliverables: Developing the Optimal Documentation ...Scott Abel
Presented by Nicki Bleiel at Documentation and Training LIfe Sciences, June 23-26, 2008 in Indianapolis.
Documentation deliverables have evolved beyond manuals and online help in recent years, and with the emergence of Web 2.0, things are changing faster than ever. Technical communicators have many more options to enhance the user experience, and developing many of them provide the opportunity to work with other departments to find a more holistic approach to content development and delivery. But there is no one-size-fits-all set of solutions. This workshop will review the types of analysis you need to do to determine which deliverables are right for your project, your customer, and your company.
Other factors that can’t be ignored, such as translation needs, staff/time constraints, file size limitations, corporate image and control, and proprietary concerns will also be discussed, including:
Analyzing the Product
* Intended audience; delivery method (desktop, web application, etc.); competitor offerings; software development methodology. The UI as part of the Help system. Product Management expectations.
Identifying User Wants and Needs
* Preferences and expectations for information; work environment; knowledge and experience levels.
Ascertaining Internal Needs and Opportunities
* Working with Training, Support, and Marketing to reduce duplication and provide the user with consistent, useful information.
* Finding ways to incorporate information from other departments to improve documentation.
Accessing Deliverable Options
* What is the optimum mix for the product?
* The traditional: online help, manuals, embedded help, job aids, forums, web sites, technical support knowledgebases.
* Emerging trends: wikis, blogs, RSS feeds, software demonstrations, podcasts, and other collaborative tools. They can supplement and/or enhance the traditional. Or, they may be a better fit for internal knowledge management or marketing use.
Optimizing the Library
* Single-sourcing; best practices for structuring information; continuous publishing
Analyzing Your Deliverables: Developing the Optimal Documentation LibraryScott Abel
Presented Nicki Bleiel at Documentation and Training Life Sciences, June 23-26, 2008 in Indianapolis.
Documentation deliverables have evolved beyond manuals and online help in recent years, and with the emergence of Web 2.0, things are changing faster than ever. Technical communicators have many more options to enhance the user experience, and developing many of them provide the opportunity to work with other departments to find a more holistic approach to content development and delivery. But there is no one-size-fits-all set of solutions. This workshop will review the types of analysis you need to do to determine which deliverables are right for your project, your customer, and your company. Product analysis, user expectations and needs, internal needs, deliverable options, and optimizing your library will all be discussed; as well as translation needs, staff/time constraints, file size limitations, corporate image and control, and proprietary concerns.
Evolution of Collaborative Content Management
Even as IT spending slides, IT departments are having to handle more content, more users, provide more productivity, as well as more compliance.
They have to do all this at less cost.
The enterprise hasn't kept up with advances in collaboration - most still uses a shared drive + MS Office + Email.
Most knowledge workers:
- can't find documents
- can't find the right version of documents
- find it easier to search for competitors' info than their own company's info
- have lots more noise than signal
This presentation talks about the collaborative enteprise. Essentially providign Facebook-like features for the Enterprise.
● The Cost of Poor Collaborative Content Management
● Best Practice – On the Web and in the Enterprise
● Alfresco
● Alfresco Share
● Standards Support
● Total Cost of Ownership
● More Information
Intranet 2.0 - Integrating Enterprise 2.0 into your corporate intranetJames Dellow
Enterprise 2.0 opportunities and challenges; The technology building blocks: Blogs, RSS,
tags, search and wikis; Implementation approaches: Nature or nurture? Pulling it all together and getting started.
This presentation was made as a workshop at Intranet '07 on 20th September, 2007 in Sydney, Australia. Note: This version of the presentation pack contains only key slides and omits additional reading materials provided.
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.
2010 June 13
Keynote talk given at the
Workshop for Modeling Social Media
ACM Hypertext 2010 Conference
Presenter: Ed H. Chi
Talk Title:
Model-driven Research for Augmenting Social Cognition
Short Abstract:
Model-driven research seeks to predict and to explain the phenomena in systems. The drive to do this for social computing research should further our understanding of how these systems evolve and develop. I will illustrate how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I will also show how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia's contribution patterns.
Using Information Scent to Model Users in Web1.0 and Web2.0Ed Chi
This talk summarizes the work I have been doing on modeling user behavior on Web1.0 and Web2.0 systems in the last 13 years
Talk given at a workshop on Cognitive Modeling in Utrecht, Netherlands on March 20, 2010.
2010-03-10 PARC Augmented Social Cognition Research OverviewEd Chi
This is an overview of the 3-year research works done at the Augmented Social Cognition research group at PARC.
See blog at:
http://asc-parc.blogspot.com
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Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
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www.seribangash.com
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https://seribangash.com/promotors-is-person-conceived-formation-company/
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https://seribangash.com/difference-public-and-private-company-law/
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2007 KMWorld Presentation on Augmented Social Cognition Research at PARC
1. Web 2.0 in the Enterprise
Improving collaboration through research insights
in coordination costs
KMWorld & Intranets 2007
November 7, 2007
Ed H. Chi, Ph.D.
Manager, Augmented Social Cognition Area
echi@parc.com
Lawrence C. Lee
Director of Business Development
lawrence.lee@parc.com
Intelligent Systems Laboratory
Palo Alto Research Center Inc.