Employing so as to improve Client Experience Aggregate Knowledge

Enhancing user experience by employing collective intelligence l.jpg
1 / 42
0
0
1409 days ago, 465 views
PowerPoint PPT Presentation
Google Trends. Asks:

Presentation Transcript

Slide 1

Improving User Experience By Employing Collective Intelligence April 16, 2008 Jason Zietz

Slide 2

Meet the Presenter Education M.S. Software engineering and Application, Virginia Tech Thesis: Activity-based Knowledge Management Tool Design for Educators Work Experience Companies huge and little Currently User Experience expert

Slide 3

Presentation Overview Background Examples of Collective Intelligence Implementing Collective Intelligence Applications in Current L3D Research

Slide 4

What is Collective Intelligence? Aggregate insight is a type of knowledge that rises up out of the coordinated effort and rivalry of numerous people. (Wikipedia) Necessary Ingredients from Participants: Appropriate outlook Willingness to share Openness to the estimation of circulated knowledge for the benefit of all

Slide 5

Why Do We Care About Collective Intelligence on the Web? Flag versus Clamor in the Long Tail

Slide 6

Why Do We Care About Collective Intelligence Now?

Slide 7

What Is User Experience?

Slide 8

Why Do We Care About UX?

Slide 9

Why Do We Care About UX?

Slide 10

Why Is UX Important to Collective Intelligence (and the other way around)? Utility = Value/Effort " Reservoir of Goodwill " (Krug)

Slide 11

Presentation Overview Background Examples of Collective Intelligence Implementing Collective Intelligence Applications in Current L3D Research

Slide 12

Explicit versus Verifiable Activities Implicit Insight accomplished innately with no additional work from the client Explicit Insight requires particular movement from client

Slide 13

Common Computer-based Collective Intelligence Applications Social Networks Discussion Forums Mailing Lists Rating Systems Tags

Slide 14

Google Search A goliath proposal framework Condor (Gloor) Google Trends Asks: "What are individuals looking for?" Takes Google Search above and beyond

Slide 15

Amazon System Activity Home page suggestions "Individuals who purchased this likewise acquired… " "Purchase this with this and get an extra 5% off" User Activity Item Viewing Purchasing "I Own It" Control (Yes/No) Rating System (1-5 Scale) Was this survey accommodating? (Yes/No) Tags

Slide 16

Netflix Recommendations and the Netflix Prize $1,000,000 to contestant scoring 10% superior to Netflix's Cinematch proposal framework Began as a crowdsourcing try however turned into a wellspring of aggregate knowledge 12/2006 – Third place participant posted finish calculation online Netflix has joined thoughts from current pioneer into Cinematch Just a Guy in a Garage

Slide 17

flickr

Slide 18

flickr

Slide 19

Other Examples Open Source Software del.icio.us – Social bookmarking by means of labeling Wikipedia – When crowdsourcing gets to be aggregate insight Digg Visualizations – Was UX overlooked?

Slide 20

Presentation Overview Background Examples of Collective Intelligence Implementing Collective Intelligence Applications in Current L3D Research

Slide 21

User Experience Tasks Requirements Gathering Task Flows/Wireframing/Prototyping Testing Evaluation

Slide 22

Programming Collective Intelligence Using Tags Identification Searching Tag Clouds Not Using Tags UX Consideration

Slide 23

Programming Collective Intelligence Making Recommendations Similarity Coefficients Euclidean Distance Pearson Correlation Tanimoto Similarity Score Others ( Jaccard , Manhattan , and so on) Cognitive Biases

Slide 24

Euclidean Distance Used in appraisals frameworks Straight-line separate between two focuses Can be utilized to quantify distinction in evaluations by two individuals To get a comparability score between two individuals, figure which yields a number somewhere around 0 and 1, where 1 implies that the two individuals appraised the greater part of the things indistinguishably

Slide 25

Pearson Similarity Coefficient Measure of how well two arrangements of information fit on a straight line Correlation of 1 means appraisals were indistinguishable

Slide 26

Tanimoto Similarity Score Where N A : Total things in A N B : Total things in B N C : Total things in both An and B Tanimoto Similarity Score is the proportion of the crossing point set to the union set

Slide 27

Cognitive Biases Psychological Effects That Can Skew Data Example: Anchoring in Netflix evaluations

Slide 28

Clustering Prepare information utilizing normal arrangement of numerical ascribes used to look at things Choose grouping technique Hierarchical Clustering K-Means Clustering

Slide 29

Hierarchical Clustering

Slide 30

K-Means Clustering

Slide 31

Clustering Blogs with Hierarchical Clustering

Slide 32

Visualizing Clusters - Dendograms

Slide 33

Clustering Blogs with Hierarchical Clustering

Slide 34

Clustering Words inside Blogs with Hierarchical Clustering

Slide 35

Presentation Overview Background Examples of Collective Intelligence Implementing Collective Intelligence Applications in Current L3D Research

Slide 36

Applications to Meta-Design Meta-Design Explores Personally Meaningful Activities Output of aggregate knowledge applications must be important to members Meta-Design Requires Active Contributors Collective insight applications take into account an extensive variety of action, from certain to exceptionally unequivocal commitments Meta-Design Raises Research Problems, Including Collaboration and Motivation Collective insight applications can empower understood joint effort Collective knowledge applications can yield comes about generally not seen by members, along these lines expanding utility and decidedly impacting inspiration

Slide 37

Applications in 'Transformative Models of Learning… " Why endeavor to enhance UX through Collective Intelligence in this examination? As the span of a VO scales upwards, the capacity to effectively recognize associations among individuals and find significant data diminishes Aiming to Create a VO of Active Contributors Utility = Value/Effort

Slide 38

Applications in 'Transformative Models of Learning… " Link Members of VO Activity: Members of VO label themselves Tags – Skills they have, abilities they need (yet have use for), research premiums Use Tanimoto score to match individuals with comparable research premiums Use Tanimoto score to match individuals who do not have an aptitude with individuals who have that expertise

Slide 39

Applications in 'Transformative Models of Learning… " Discover Relevant Areas of Study Activity: Rate coursework taken System stores past coursework of all members Students can rate this coursework as indicated by the amount they enjoyed the subject System utilizes appraisals to propose different zones of study which might enthusiasm to understudy

Slide 40

Applications in 'Transformative Models of Learning… " Explore Relevant Content Activity: Cluster content inside VO Allow individuals from VO to investigate important substance in groups utilizing perceptions, for example, dendograms

Slide 41

Applications in "… Using and Evolving Software Products" Increase Utility of SAP Message Boards Cluster related messages and permit clients to investigate the messages through an intelligent dendogram Make proposals of strings clients might be keen on perusing

Slide 42

Suggested Readings Blog of Collective Intelligence (Pór) Programming Collective Intelligence (Segaran) Peter Morville on User Experience Design Elements of User Experience (Garrett) The Machine is Us/ing Us

SPONSORS