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.
Summary of Research Supporting the SCERTS® ModelMedical Net Srl
An interesting document with references on the use and results of the SCERTS model.
The SCERTS model is
based on a developmental framework and has incorporated evidence-based practices from the
recommendations of the National Research Council (NRC, 2001) based on its review of
educational treatments for children with ASD and more current research.
EUDAT Webinar "Organise, retrieve and aggregate data using annotations with B...EUDAT
| www.eudat.eu | Annotate your research data with B2NOTE:
A note in the margins of a book or a scientific paper, a comment on a manuscript: we are all using annotations to add information to existing physical documents. To offer a similar experience with digital content within the EUDAT Collaborative Data Infrastructure (CDI), we developed a service that allows associating additional information to a file, in a computer-readable format, without changing the file or the data record itself. These digital annotations can thus be searched to organize, retrieve and aggregate files, datasets and documents.
Although B2NOTE is a standalone service, it has been designed to be integrated with the existing EUDAT services. In the first pilot version, B2NOTE allows to annotate files located in B2SHARE. The service is called as a “widget” within the B2SHARE User Interface. B2NOTE allows you to easily and intuitively create three types of annotations: a semantic tag coming from identified ontology repositories (only Bioportal at the moment but we are working toward integrating more vocabularies), a free-text keyword that can be used when you do not find a semantic term in particular and a free-text comment.
ICT research in the context of European Union
CASE SUMMER SCHOOL ON APPLIED SOFTWARE ENGINEERING
APPLIED SOFTWARE PROCESS MANAGEMENT AND TESTING
JULY 6-10, 2009, BOZEN/BOLZANO, ITALY
Summary of Research Supporting the SCERTS® ModelMedical Net Srl
An interesting document with references on the use and results of the SCERTS model.
The SCERTS model is
based on a developmental framework and has incorporated evidence-based practices from the
recommendations of the National Research Council (NRC, 2001) based on its review of
educational treatments for children with ASD and more current research.
EUDAT Webinar "Organise, retrieve and aggregate data using annotations with B...EUDAT
| www.eudat.eu | Annotate your research data with B2NOTE:
A note in the margins of a book or a scientific paper, a comment on a manuscript: we are all using annotations to add information to existing physical documents. To offer a similar experience with digital content within the EUDAT Collaborative Data Infrastructure (CDI), we developed a service that allows associating additional information to a file, in a computer-readable format, without changing the file or the data record itself. These digital annotations can thus be searched to organize, retrieve and aggregate files, datasets and documents.
Although B2NOTE is a standalone service, it has been designed to be integrated with the existing EUDAT services. In the first pilot version, B2NOTE allows to annotate files located in B2SHARE. The service is called as a “widget” within the B2SHARE User Interface. B2NOTE allows you to easily and intuitively create three types of annotations: a semantic tag coming from identified ontology repositories (only Bioportal at the moment but we are working toward integrating more vocabularies), a free-text keyword that can be used when you do not find a semantic term in particular and a free-text comment.
ICT research in the context of European Union
CASE SUMMER SCHOOL ON APPLIED SOFTWARE ENGINEERING
APPLIED SOFTWARE PROCESS MANAGEMENT AND TESTING
JULY 6-10, 2009, BOZEN/BOLZANO, ITALY
IUI 2010: An Informal Summary of the International Conference on Intelligent ...J S
Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops. This is an informal compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and other sources. Citations were not coherently possible, so I chose to stick with links instead. Please let me know if you'd like to see your work more thoroughly referenced.
PATHS state of the art monitoring reportpathsproject
This document provides an update to an Initial State of the Art Monitoring report delivered by the project. The report covers the areas of Educational Informatics, Information Retrieval and Semantic Similarity relatedness.
On 2008-11-15 Maurice Vanderfeesten gave a presentation in Baltimore at the SPARC OpenAccess confenrence.
This presentation explains about the needs for interoperability amoung repository systems. DRIVER provides guidelines how to expose metadata via OAI-PMH is a way that has international compliance.
GVIS: a framework for graphical mashups of heterogeneous sources to support d...Luca Mazzola
This paper introduces the GVIS framework and describes one of its applications built in support of user profile awareness. This application is aimed at opening part of users' profiles to their inspection by exploiting a graphical representation of their personal data. We developed an infrastructure for presenting these high level information in a configurable and adaptable way. The framework we developed is able to retrieve data from heterogeneous sources just by writing a small adapter and allows us to mix together different streams through an XML configuration that relies on a set of operations for elicitation of the most interesting fragments. The final goal is to provide an easily readable graphical representation of the most relevant information, in order to support the human visual system, more capable to have an overview with this kind of solution than with text. As an example application we have mashed up URLs from user browsing history with tags coming from del.icio.us: the resulting output, represented as a pie chart, shows the most relevant subjects followed by a user. Some open issues and problems, we hope to research next, are presented in the conclusion part.
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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IUI 2010: An Informal Summary of the International Conference on Intelligent ...J S
Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops. This is an informal compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and other sources. Citations were not coherently possible, so I chose to stick with links instead. Please let me know if you'd like to see your work more thoroughly referenced.
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This document provides an update to an Initial State of the Art Monitoring report delivered by the project. The report covers the areas of Educational Informatics, Information Retrieval and Semantic Similarity relatedness.
On 2008-11-15 Maurice Vanderfeesten gave a presentation in Baltimore at the SPARC OpenAccess confenrence.
This presentation explains about the needs for interoperability amoung repository systems. DRIVER provides guidelines how to expose metadata via OAI-PMH is a way that has international compliance.
GVIS: a framework for graphical mashups of heterogeneous sources to support d...Luca Mazzola
This paper introduces the GVIS framework and describes one of its applications built in support of user profile awareness. This application is aimed at opening part of users' profiles to their inspection by exploiting a graphical representation of their personal data. We developed an infrastructure for presenting these high level information in a configurable and adaptable way. The framework we developed is able to retrieve data from heterogeneous sources just by writing a small adapter and allows us to mix together different streams through an XML configuration that relies on a set of operations for elicitation of the most interesting fragments. The final goal is to provide an easily readable graphical representation of the most relevant information, in order to support the human visual system, more capable to have an overview with this kind of solution than with text. As an example application we have mashed up URLs from user browsing history with tags coming from del.icio.us: the resulting output, represented as a pie chart, shows the most relevant subjects followed by a user. Some open issues and problems, we hope to research next, are presented in the conclusion part.
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
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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.
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
2010-02-22 Wikipedia MTurk Research talk given in Taiwan's Academica SinicaEd Chi
This is the talk I gave at the Academica Sinica Inst. for Information Science in Taiwan. It focuses on our Wikipedia and Amazon Mechanical Turk research.
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Using Information Scent to Model Users in Web1.0 and Web2.0
1. Modeling of Web Users from Web1.0 to Web2.0 Ed H. Chi, Principal Scientist and Area Manager Augmented Social Cognition Area Palo Alto Research Center Image from: http://www.flickr.com/photos/ourcommon/480538715/ 2010-03-20 Utrecht CogModeling
2.
3.
4.
5.
6. Analogy to Optimal Foraging 2010-03-20 Utrecht CogModeling Information Energy
7.
8.
9. 2010-03-20 Utrecht CogModeling i bread j butter sandwich flour Ai = Bi + WjSji Activation of chunk i Base-level activation of chunk i Activation spread from linked chunks j Activation depends on a base level plus activation spread from associated chunks Bi = log( ) Pr( i ) Pr(not i ) Sji = log( ) Pr( j | i ) Pr( j |not i ) log likelihood of i occurring log likelihood of i occurring with j Base level activation reflects log likelihood of events in the world. Strength of spread reflects log likelihood of event cooccurrance
10.
11. WUFIS: Web User Flow by Information Scent 2010-03-20 Utrecht CogModeling User Information Goal Web site Web Page content links Web user flow simulation Predicted paths
12. InfoScent: How does it work? Utrecht CogModeling Start users at page with some goal Flow users through the network Examine user patterns Scent Values: Probabilities of Transition 2010-03-20
13. InfoScent Simulation Utrecht CogModeling 2 1 2010-03-20 Now with the Scent Matrix, we then perform Spreading Activation. 3 Weight Matrix Query Relevant Docs R = Relevant documents T = Topology matrix Normalize to Probability Scent Matrix
14.
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16. Bloodhound Project 2010-03-20 Utrecht CogModeling Starting Point: www.xerox.com Task: look for “ high end copiers ” OUTPUT usability metrics INPUT
24. IUNIS: Inferring User Need by Info Scent 2010-03-20 Utrecht CogModeling User Information Goal Web site Web Page content links Web user flow simulation Observed paths
29. Web page with highlighted link anchors 2010-03-20 Partial information goal: “ remote diagnostic technology” Remainder of information goal: “ speed >= 75” Utrecht CogModeling 62 copies/min. 92 copies/min.
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31. Results of user study Utrecht CogModeling (times capped at five minutes) 10/12 subjects preferred ScentTrails to both searching and browsing 2010-03-20
33. ScentHighlight User first type search keywords: “anthrax symptoms” Conceptually highlight any relevant passages and keywords Draw user attention 2010-03-20 Utrecht CogModeling
43. Using Information Theory to Model Social Tagging [Ed H. Chi, Todd Mytkowicz, ACM Hypertext 2008] Topics Users Documents Decoding 2010-03-20 Utrecht CogModeling Concepts Tags T 1 …T n Encoding Noise
50. TagSearch: Use Semantic Analysis to Reduce Noise http://mrtaggy.com 2010-03-20 Utrecht CogModeling Guide Web Howto Tips Help Tools Tip Tricks Tutorial Tutorials Reference Semantic Similarity Graph
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52. Understanding a new area… 2010-03-20 Characterization Models Prototypes Evaluations Utrecht CogModeling
53. MrTaggy.com: social search browser with social bookmarks 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. 2010-03-20 Utrecht CogModeling
67. TagSearch Exploratory Focus 3 kinds of search 2010-03-20 Utrecht CogModeling navigational transactional 28% 13% You know what you want and where it is You know what you want to do Existing search engines are OK informational 59% You roughly know what you want but don’t know how to find it Difficult for existing search engines Opportunity
Editor's Notes
Title: Modeling of Web Users from Web1.0 to Web2.0 Abstract: In this talk, I will provide a perspective on how information scent techniques have taken us to characterize and model individual web surfers in the Web1.0 world, and how we used those techniques to build applications and systems. Then I will present some ideas of we might bridge these ideas to the Web2.0 world by modeling groups of users using Web2.0 systems.
. Example: Media news is fresh. With the right interest, users have a high probability of following that piece of information. . Hunters strategies maximizes the benefit per cost of pursuing the prey. Information gatherers do exactly the same thing.
. Example: Media news is fresh. With the right interest, users have a high probability of following that piece of information. . Hunters strategies maximizes the benefit per cost of pursuing the prey. Information gatherers do exactly the same thing.
Statistically, a correlation coefficient above 0.8 is generally considered to be strong correlation, and between 0.5 and 0.8 is considered moderate, while below 0.5 is considered weak correlation . Twelve correlated strongly, and seventeen of the 32 tasks correlated moderately.
. Using our technology, by telling the web site of your special requirements, each virtual aisle of the web site is pre-highlighted according to your special request, making it easier for you to shop.
In the enterprise, these have become the standard set of Web 2.0 tools in practice. They have several benefits – they can be set up by end users without needing IT, they have familiar UIs from consumer versions, And in terms of knowledge sharing, an important advantage these tools have over traditional KM systems is that knowledge can be captured and archived through the act of communication without requiring extra work by users. These tools will become increasingly important in the office as younger people enter the workforce and expect to be able to use them.
There are really two facets of tagging. The first is encoding: when you encounter a document, have read or skimmed it and have to generate a few words that describe it. The second side of tagging is retrieval: you find a new document that has several tags attached to it, and you read those tags and the document. The tags may give you an idea about what the document is about. I am going to come back to this distinction later.
Vocabulary saturation! shows a marked increase in the entropy of the tag distribution H(T) up until week 75 (mid-2005) at which point the entropy measure hits a plateau. Since the total number of tags keeps increasing, tag entropy can only stay constant in the plateau by having the tag probability distribution become less uniform. What this suggests is that users are having a hard time coming up with “unique” tags. That is to say, a user is more likely to add a tag to del.icio.us that is already popular in the system, than to add a tag that is relatively obscure.
What’s perhaps the most telling data of all is the entropy of documents conditional on tags, H(D|T) , which is increasing rapidly (see Figure 4). What this means is that, even after knowing completely the value of tags, the entropy of the document is still increasing. Conditional Entropy asks the question: “Given that I know a set of tags, how much uncertainty regarding the document set that I was referencing with those tags remains?” This measure gives us a method for analyzing how useful a set of tags is at describing a document set. The fact that this curve is strictly increasing suggests that the specificity of any given tag is decreasing. That is to say, as a navigation aid, tags are becoming harder and harder to use. We are moving closer and closer to the proverbial “needle in a haystack” where any single tag references too many documents to be considered useful.
Figure 6 shows the number of tags per bookmark over time. The trend is clearly increasing, complementing the increase in navigation difficulty.
We introduce a technique for creating novel, textually-enhanced thumbnails of web pages. These thumbnails combine the advantages of image thumbnails and text summaries to provide consistent performance on a variety of tasks. We conducted a study in which participants used three different types of summaries (enhanced thumbnails, plain thumbnails, and text summaries) to search web pages to find several different types of information. Participants took an average of 83 seconds to find the answer to a question. They were approximately 30 seconds faster with enhanced thumbnails than with text summaries, and 19 seconds faster with enhanced thumbnails than with plain thumbnails. Further, performance with enhanced thumbnails was much more consistent than with text summaries or plain thumbnails. In the images shown on this slide, the top row contains plain (scale-reduced) thumbnails of web pages. The bottom row contains thumbnails that have been enhanced in the following way: (1) the fonts in H1 and H2 tags have been modified so that they are readable in the thumbnails; (2) transparent, highlighted callouts have been included for keywords from the search query (appropriate highlighted colors were chosen based on visual attention models); and (3) the contrast level in the thumbnail has been reduced so that the callouts are more prominent and readable.
Informational search – ambiguity in query – where social search has most power