Personalizing Netflix

    A brief history
            Jon Sanders
 Recommendation Systems Engineering
               Netf...
Fun facts about Netflix
World’s largest online movie rental service   #1 in customer satisfaction
Founded 1997            ...
In the beginning…




 Everyone sees the same site
Evolve methodically
The rating widget

•  Ask about & predict movie Enjoyment

•  User-similarity collaborative filter

•  Recommendations fue...
Score & sort any movie




Combine popularity & enjoyment prediction
Tune recommendations
•  Movie-similarity collaborative filter

•  K-nearest-neighbor algorithm

•  More credible connectio...
Interest-based discovery




Metadata connections: actor, director, genre, …
Ask about Interest




Moderate prominence of catalog areas
Ask other people




Community offers decision support
Explain why




Build trust with reflected evidence
$1M Netflix Prize
•  Improve accuracy of Enjoyment predictions
   –  100M ratings
   –  Achieve 10% better than Netflix RM...
A website for each subscriber
Evolution continues
•  Tailor with more metadata, implicit data
•  Streaming-specific personalization




•  Collaborative...
Links

•    http://jobs.netflix.com
•    http://www.netflix.com/ContactUs
•    http://www.netflixprize.com
•    http://www...
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Jon Sanders on Collaborative Filters at SXSW

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Jon Sanders of Netflix presenting "Collaborative Filters: The Evolution of Recommendation Engines" at SXSW Interactive, March 14 2009

Published in: Technology, Business

Jon Sanders on Collaborative Filters at SXSW

  1. Personalizing Netflix A brief history Jon Sanders Recommendation Systems Engineering Netflix Los Gatos, CA jsanders @ netflix.com http://jobs.netflix.com
  2. Fun facts about Netflix World’s largest online movie rental service #1 in customer satisfaction Founded 1997 Video rental companies (Consumer Reports) Online retail (ForeSee) With more than… On an average day 10M subscribers, $1B revenue 2M DVDs shipped 100K DVD titles, 50 distribution centers 2M movie ratings received 12K streaming movies & TV episodes 1.5B minutes streamed to 1M Xbox360’s 2B movie ratings 60% of movies selected based on personalized recommendations Connecting people with movies they’ll love
  3. In the beginning… Everyone sees the same site
  4. Evolve methodically
  5. The rating widget •  Ask about & predict movie Enjoyment •  User-similarity collaborative filter •  Recommendations fuel discovery
  6. Score & sort any movie Combine popularity & enjoyment prediction
  7. Tune recommendations •  Movie-similarity collaborative filter •  K-nearest-neighbor algorithm •  More credible connections
  8. Interest-based discovery Metadata connections: actor, director, genre, …
  9. Ask about Interest Moderate prominence of catalog areas
  10. Ask other people Community offers decision support
  11. Explain why Build trust with reflected evidence
  12. $1M Netflix Prize •  Improve accuracy of Enjoyment predictions –  100M ratings –  Achieve 10% better than Netflix RMSE •  Innovative, engaged research community •  Highly relevant results –  Global and time-based corrections –  SVD, RBM models –  Blending predictors
  13. A website for each subscriber
  14. Evolution continues •  Tailor with more metadata, implicit data •  Streaming-specific personalization •  Collaborative Filtering is a component of personalization •  People want to drive, not be led •  Offer discovery, focus and decision support http://jobs.netflix.com
  15. Links •  http://jobs.netflix.com •  http://www.netflix.com/ContactUs •  http://www.netflixprize.com •  http://www.netflix.com/netflixfindyourvoice •  http://en.wikipedia.org/wiki/Collaborative_filtering
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