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Why would you recommend me THAT?
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Why would you recommend me THAT?

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With so many advances in machine learning recently, it’s not unreasonable to ask: why aren’t my recommendations perfect by now?

Aish provides a walkthrough of the open problems in the area of recommender systems, especially as they apply to Netflix’s personalization and recommender algorithms. He also provides a brief overview of recommender systems, and sketches out some tentative solutions for the problems he presents.

Publicado en: Datos y análisis
  • Sé el primero en comentar

Why would you recommend me THAT?

  1. 1. R ≈ UM
  2. 2. R ≈ UM
  3. 3. K U P α θ φz r β
  4. 4. Convex combination of topics proportions and movie proportions within topic
  5. 5. φ3φ2 φ1 User
  6. 6. ?
  7. 7. P(rel|usr,vid) ≈ P(cons|usr,vid) = {1,0}
  8. 8. P(cons|pos,evi,rel)P(pos|rel)P(evi|usr,vid) P(rel|usr,vid,obv-ctxt,unobv-ctxt) PositionTheir final decision Evidence The recommender
  9. 9. P(cons|pos,evi,rel)P(pos|rel)P(evi|usr,vid) P(rel|usr,vid,obv-ctxt,unobv-ctxt)
  10. 10. TakeRate = #plays / #presentations
  11. 11. P(cons|...) = {1,?}
  12. 12. N N P P N ? P ? ? ? ? N ? ? ? ? ?? ?
  13. 13. ? ? P P ? ? P ? ? ? ? ? ? ? ? ? ?? ?
  14. 14. P(cons|pos,evi,rel)P(pos|rel)P(evi|usr,vid) P(rel|usr,vid,obv-ctxt,unobv-ctxt) Can’t do anything about this bit
  15. 15. RU M u1 u2 u3 u4 m1 m2 m3 m4 Tri,j,1 ri,j,2 ri,j,3 t1 t2 t3
  16. 16. P(cons|pos,evi,rel)P(pos|rel)P(evi|rel) P(rel|usr,vid,obv-ctxt,unobv-ctxt)
  17. 17. KickAssLadies Sci-Fi Exciting Super Heros Netflix Original Stars: Krysten Ritter Detective Show
  18. 18. R’U M u1 u2 u3 u4 m1 m2 m3 m4 ri,j
  19. 19. + ≠
  20. 20. P( | )
  21. 21. itm usr posevi con rel ctx

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