Netflix is the world's largest online movie rental service with over 10 million subscribers and $1 billion in revenue. It was founded in 1997 and has evolved its recommendation system over time, starting with asking users for ratings and using collaborative filtering to provide personalized recommendations, which now account for 60% of movies selected. The $1 million Netflix Prize helped improve the accuracy of enjoyment predictions through the use of collaborative filtering, time-based corrections, and other models. Netflix continues to evolve its personalization and tailor recommendations with more metadata and implicit data.

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