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 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)