Anton Kast on Collaborative Filters at SXSW
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Collaborative filtering uses input from many users to filter information and provide recommendations. It originated in research projects in the early 1990s that allowed users to rate messages and content. This technique is now widely used to provide personalized recommendations through systems like recommendation engines, spam filters, search engines, social tagging, and comment moderation. Recommender systems like Amazon and Netflix use collaborative filtering to provide personalized product and media recommendations to users.
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Anton Kast on Collaborative Filters at SXSW
- 2. Old Terms with Origins in Research ( user annotations, ) “Tapestry” email filter at Xerox PARC, 1992 search grammar (readers rate messages,) “GroupLens” usenet filter at UMN, 1994 correlates people (social actual business, ) an “Firefly” music and friend recommender at MIT, 1995 network elements Research origins led to sophisticated implementations, but the idea is simple. Collaborative Filters: Recommendation Engines
- 3. The Idea of Collaborative Filtering Combine input from many different people to filter information better than would otherwise be possible. Collaborative Filters: Recommendation Engines
- 4. This Technique is Everywhere Spam filters Help systems Pagerank Click feedback in search ranking Tagging Facebook ads Comment moderation Thumbs on everything Collaborative Filters: Recommendation Engines
- 5. When it’s Personalized Call it ‘Recommendation’ Music, movie, book sales Customized search results Behavioral ad targeting Google News Amazon’s recursion: filtered recommendations! Collaborative Filters: Recommendation Engines
- 6. How Digg Works 1. Anyone can submit a story 2. Anyone can vote on any story 3. Most popular recent stories win (classic collaborative filtering) 4. If you sign up, you get personalized stories too (recommendations) Collaborative Filters: Recommendation Engines
- 9. Where we can Leave the Rails The sparsity problem Submissions can grow faster than active diggers The early-rater problem We have no way to jump-start the recommendation cycle Gray sheep We have smaller sub-communities with unpopular views User opposition A few times, we have simply come to loggerheads
- 10. Introductions Anton Kast, Digg Erik Frey, Last.fm Scott Brave, Baynote David Maher Roberts, TheFilter Jon Sanders, Netflix