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Thesis presentation on online recommendations at scale with matrix factorisation

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Marcus Ljungblad

This presentation was used for my thesis defense held at Universidad Politecnica de Catalunya, Spain, for a double-degree master programme in Distributed Computing. The other two universities participating in the programme are Royal Institute of Technology, Stockholm, Sweden and Instituto Tecnico Superior, Lisbon, Portugal.

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Thesis presentation: Online recommendations at scale with matrix factorisation. Royal Institute of Technology, Stockholm, Sweden 22 June 2012 Instituto Superior Técnico, Lisbon, Portugal Marcus Ljungblad Universitat Politécnica de Catalunya, Barcelona, Spain marcus@ljungblad.nu
" 75% of the 30 million daily movie starts are sourced from recommendations.
" a key differentiating factor
3 challenges
How do you serve recommendations from millions of items to millions of users online?
Video ratings 2 4 4 ? 1 3 5 ? ? 1 Users ? 4 2 1 ? 1 ? 1 3 3
f( )
Video ratings 2.05 3.97 3.96 2.12 1.01 2.93 5.02 3.21 1.61 0.98 Users 2.15 3.95 2.01 1.05 1.10 1.00 4.29 1.01 2.96 2.98
Video ratings 2.05 3.97 3.96 2.12 1.01 2 4 4 ? 1 Users 2.93 5.02 3.21 1.61 0.98 3 5 ? ? 1 2.15 3.95 2.01 1.05 1.10 ? 4 2 1 ? 1.00 4.29 1.01 2.96 2.98 1 ? 1 3 3
2.05 3.97 3.96 2.12 1.01 2.93 5.02 3.21 1.61 0.98 2.15 3.95 2.01 1.05 1.10 1.00 4.29 1.01 2.96 2.98
13x40 MILLION RATINGS
Interface Delegate Router Worker request start route compute top-N merge to json reply
Interface Delegate Router Worker request start route compute top-N merge to json reply
Did it work?
Setup: • 1-3 machines • 1 million items • same rack = high-speed • 1 test machine
Performance!
Performance! h uh?!
Did it work? w ell
74% = 74% Offline Online
Summary: ... clustering depends on data ... ... need balanced clusters ... ... memory bound ... ... scales ok ...
Thank you!
Photos and pictures borrowed from the Internetz: Iron Maiden cover: http://en.wikipedia.org/wiki/File:Iron_Maiden_(album)_cover.jpg Cat picture: http://www.lastfm.es/group/Cats Coins: http://www.sxc.hu/photo/1235540 iPhones: http://blog.bayuamus.com/2011/08/user-experience-comparison-between-htc-salsa-and-samsung-galaxy-mini/ Amazon recommendations: http://mashable.com/2010/08/06/online-retail-facebook-data/ TV remote: http://www.flickr.com/photos/62337512@N00/2749561795/sizes/z/in/photostream/ Headphones: http://www.flickr.com/photos/markusschoepke/82957375/sizes/m/in/photostream/ Function: http://en.wikipedia.org/wiki/File:Graph_of_example_function.svg Home servers: http://www.flickr.com/photos/fabrico/477844434/sizes/z/in/photostream/
Extra material...
AXYDBLSZQ (1/2) / 1 AXYDBLSZQ (1/1) / 1 AXYDBLSZQ (1/1 + 2/3) / 2

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Thesis presentation on online recommendations at scale with matrix factorisation

  • 1. Thesis presentation: Online recommendations at scale with matrix factorisation. Royal Institute of Technology, Stockholm, Sweden 22 June 2012 Instituto Superior Técnico, Lisbon, Portugal Marcus Ljungblad Universitat Politécnica de Catalunya, Barcelona, Spain marcus@ljungblad.nu
  • 2.
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  • 5. " 75% of the 30 million daily movie starts are sourced from recommendations.
  • 6. " a key differentiating factor
  • 8.
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  • 14. How do you serve recommendations from millions of items to millions of users online?
  • 15. Video ratings 2 4 4 ? 1 3 5 ? ? 1 Users ? 4 2 1 ? 1 ? 1 3 3
  • 16. f( )
  • 17. Video ratings 2.05 3.97 3.96 2.12 1.01 2.93 5.02 3.21 1.61 0.98 Users 2.15 3.95 2.01 1.05 1.10 1.00 4.29 1.01 2.96 2.98
  • 18. Video ratings 2.05 3.97 3.96 2.12 1.01 2 4 4 ? 1 Users 2.93 5.02 3.21 1.61 0.98 3 5 ? ? 1 2.15 3.95 2.01 1.05 1.10 ? 4 2 1 ? 1.00 4.29 1.01 2.96 2.98 1 ? 1 3 3
  • 19. 2.05 3.97 3.96 2.12 1.01 2.93 5.02 3.21 1.61 0.98 2.15 3.95 2.01 1.05 1.10 1.00 4.29 1.01 2.96 2.98
  • 21.
  • 22.
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  • 29. Interface Delegate Router Worker request start route compute top-N merge to json reply
  • 30. Interface Delegate Router Worker request start route compute top-N merge to json reply
  • 32. Setup: • 1-3 machines • 1 million items • same rack = high-speed • 1 test machine
  • 34. Performance! h uh?!
  • 35. Did it work? w ell
  • 36. 74% = 74% Offline Online
  • 37.
  • 38. Summary: ... clustering depends on data ... ... need balanced clusters ... ... memory bound ... ... scales ok ...
  • 40.
  • 41. Photos and pictures borrowed from the Internetz: Iron Maiden cover: http://en.wikipedia.org/wiki/File:Iron_Maiden_(album)_cover.jpg Cat picture: http://www.lastfm.es/group/Cats Coins: http://www.sxc.hu/photo/1235540 iPhones: http://blog.bayuamus.com/2011/08/user-experience-comparison-between-htc-salsa-and-samsung-galaxy-mini/ Amazon recommendations: http://mashable.com/2010/08/06/online-retail-facebook-data/ TV remote: http://www.flickr.com/photos/62337512@N00/2749561795/sizes/z/in/photostream/ Headphones: http://www.flickr.com/photos/markusschoepke/82957375/sizes/m/in/photostream/ Function: http://en.wikipedia.org/wiki/File:Graph_of_example_function.svg Home servers: http://www.flickr.com/photos/fabrico/477844434/sizes/z/in/photostream/
  • 43.
  • 44.
  • 45. AXYDBLSZQ (1/2) / 1 AXYDBLSZQ (1/1) / 1 AXYDBLSZQ (1/1 + 2/3) / 2