Online recommendations at scale using matrix factorisation
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Online recommendations at scale using matrix factorisation

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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 ...

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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Online recommendations at scale using matrix factorisation Presentation Transcript

  • 1. Thesis presentation:Online recommendationsat scale with matrix factorisation.Royal Institute of Technology, Stockholm, Sweden 22 June 2012Instituto Superior Técnico, Lisbon, Portugal Marcus LjungbladUniversitat Politécnica de Catalunya, Barcelona, Spain marcus@ljungblad.nu
  • 2. " 75% of the 30 million daily movie starts are sourced from recommendations.
  • 3. " a key differentiating factor
  • 4. 3 challenges
  • 5. How do you serverecommendationsfrom millions ofitems to millionsof users online?
  • 6. Video ratings 2 4 4 ? 1 3 5 ? ? 1Users ? 4 2 1 ? 1 ? 1 3 3
  • 7. f( )
  • 8. Video ratings 2.05 3.97 3.96 2.12 1.01 2.93 5.02 3.21 1.61 0.98Users 2.15 3.95 2.01 1.05 1.10 1.00 4.29 1.01 2.96 2.98
  • 9. Video ratings 2.05 3.97 3.96 2.12 1.01 2 4 4 ? 1Users 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
  • 10. 2.05 3.97 3.96 2.12 1.012.93 5.02 3.21 1.61 0.982.15 3.95 2.01 1.05 1.101.00 4.29 1.01 2.96 2.98
  • 11. 13x40MILLIONRATINGS
  • 12. Interface Delegate Router Workerrequest start route compute top-N merge to json reply
  • 13. Interface Delegate Router Workerrequest start route compute top-N merge to json reply
  • 14. Did it work?
  • 15. Setup: • 1-3 machines • 1 million items • same rack = high-speed • 1 test machine
  • 16. Performance!
  • 17. Performance! h uh?!
  • 18. Did it work? w ell
  • 19. 74% = 74% Offline Online
  • 20. Summary:... clustering depends on data ...... need balanced clusters ...... memory bound ...... scales ok ...
  • 21. Thank you!
  • 22. Photos and pictures borrowed from the Internetz:Iron Maiden cover: http://en.wikipedia.org/wiki/File:Iron_Maiden_(album)_cover.jpgCat picture: http://www.lastfm.es/group/CatsCoins: http://www.sxc.hu/photo/1235540iPhones: 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.svgHome servers: http://www.flickr.com/photos/fabrico/477844434/sizes/z/in/photostream/
  • 23. Extra material...
  • 24. AXYDBLSZQ (1/2) / 1AXYDBLSZQ (1/1) / 1AXYDBLSZQ (1/1 + 2/3) / 2