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KMS & Collaborative Filtering

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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KMS & Collaborative Filtering

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  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)

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