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