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
Community Centered CF
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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
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?
PHOAKS
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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
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
Context for Implicit Ratings
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Who
When
What
How (discovery)
Web Browsing
RSS Reading
Blog posting
Newsgroup- listserv use
Active CF
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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?
Sharing References
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Pointers
Packages of Information
General flexibility
Private and Public resources and ratings
Other Systems
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Fab
Tapestry
Grassroots
Epinions
eBay
Amazon (lists)