KMS

1
KMS Collaborative Filtering

• Why CF in KMS?
• CF is the first type of application to leverage
  tacit knowledge
• People-centric view of data
• Preferences matter
• Implicit
• Explicit
• Are people just data points?
• Neo-Taylorism
• Efficiency over Quality for data collection

2
Community Centered CF

• What is a community?
• Helping people find new information
• Mapping community (prefs?)
• Rating Web pages
• Recommended Web pages
• Measuring recommendation quantity?
• Measuring recommendation use
• Constant status

3
Community CF

• Personal relationships are not necessary
• What does this miss?
• If you knew about the user, would that help with
  thte cold start problem?
• Advisors
• Ratings
• Population wide
• Advisors
• Weighted sum
• How would an organization use this?

4
PHOAKS

• Wider group of people (anyone?)
• Usenet news (more text)
• Link mining for Web resources
• What counts as a recommendation?
• More than one mention?
• Positive negative?
• Fair and balanced for a Community
• How do you rank resources?
• Weights
• Topics

5
Social Affordance Implicit

• How can you not use ratings?
• Read wear, clicks, dwell time, chatter
• Not all resources are as identifiable
• Granular- Web pages
• Items - commercial products
• Web is a shared informaiton space without much
  sharing
• How do incent people to contribute?
• Social norms
• Rewards

6
Context for Implicit Ratings

• Who
• When
• What
• How (discovery)
• Web Browsing
• RSS Reading
• Blog posting
• Newsgroup- listserv use

7
Active CF

• Classic paper issues
• Leveraging what others do
• Finding what is already found?
• Take advantage of universal publishing
• How about filtering, without the collaboration?
• Individual preferences
• Implicit and Explicit
• Is wisdom being accumulated?

8
Sharing References

• Pointers
• Packages of Information
• General flexibility
• Private and Public resources and ratings

9
Other Systems

• Fab
• Tapestry
• Grassroots
• Epinions
• eBay
• Amazon (lists)