Anton Kast on Collaborative Filters at SXSW

Collaborative Filters Recommendation Engines

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

The Idea of Collaborative Filtering Combine input from many different people to ﬁlter information better than would otherwise be possible. Collaborative Filters: Recommendation Engines

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

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

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

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

Introductions Anton Kast, Digg Erik Frey, Last.fm Scott Brave, Baynote David Maher Roberts, TheFilter Jon Sanders, Netﬂix

Anton Kast on Collaborative Filters at SXSW

by Anton Kast on Mar 18, 2009

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Anton Kast on Collaborative Filters at SXSW — Presentation Transcript

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