Mind Reading - Understanding Recommender Systems

Mind Reading - Understanding Recommender Systems

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Vlad Iliescu

May 06, 2018
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  1. MIND READING - UNDERSTANDING RECOMMENDER SYSTEMS VLAD ILIESCU @

  2. A VERY IMPORTANT QUESTION™

  3. WHAT SUPER-POWER WOULD YOU LIKE?

  4. MIND READING IS NICE THIS TIME OF YEAR

  5. OFFLINE, IT’S PERSONAL

  6. ONLINE, IT DOESN’T SCALE

  7. ONLINE, IT DOESN’T SCALE UNLESS IT’S AUTOMATIC

  8. EVERYBODY’S DOING IT SO WHY CAN’T WE?

  9. REMEMBER NETFLIX’ 3-YEAR COMPETITION

  10. WHAT ARE RECOMMENDER SYSTEMS ANYWAY?

  11. THERE’S TWO TYPES OF RECOMMENDER SYSTEMS

  12. CONTENT-BASED FILTERING 1.

  13. COLLABORATIVE FILTERING 2.

  14. OKAY, MAYBE THERE’S THREE

  15. HYBRID SYSTEMS 3.

  16. LET’S GO INTO A BIT MORE DETAIL

  17. CONTENT-BASED FILTERING

  18. - Gender - Age - Preferred genres - Artist -

    Year - Genre - Song Theme (extracted)
  19. Song Alice Bob Carol Dave Genre Year The Weeknd -

    Starboy 3 5 Pop 2016 Luis Fonsi - Despacito 1 5 Dance 2017 Lorde - 400 Lux 1 2 4 5 Alternative, Pop 2013 Major Lazer - Light it Up 3 0 5 5 Dance 2016 The White Stripes - Seven Nation Army 4 1 2 1 Alternative 2003 Red Hot Chili Peppers - Scar Tissue 5 2 0 Alternative 2004 Taylor Swift - Shake it Off 3 5 2 Pop 2014
  20. Song Carol Genre Year The Weeknd - Starboy Pop 2016

    Luis Fonsi - Despacito 5 Dance 2017 Lorde - 400 Lux 4 Alternative, Pop 2013 Major Lazer - Light it Up 5 Dance 2016 The White Stripes - Seven Nation Army 2 Alternative 2003 Red Hot Chili Peppers - Scar Tissue 0 Alternative 2004 Taylor Swift - Shake it Off Pop 2014
  21. Song Carol Alternative Dance Pop Year The Weeknd - Starboy

    0 0 1 2016 Luis Fonsi - Despacito 5 0 1 0 2017 Lorde - 400 Lux 4 1 0 1 2013 Major Lazer - Light it Up 5 0 1 0 2016 The White Stripes - Seven Nation Army 2 1 0 0 2003 Red Hot Chili Peppers - Scar Tissue 0 1 0 0 2004 Taylor Swift - Shake it Off 0 0 1 2014
  22. - DOESN’T DEPEND ON OTHER USERS - RECOMMEND UNPOPULAR ITEMS

    - COLD START ISSUES ARE LESS LIKELY advantages
  23. - OVERSPECIALIZATION - DIFFICULTY EXTRACTING ATTRIBUTES FROM RICH MEDIA (IMAGES,

    AUDIO, MOVIES) disadvantages
  24. COLLABORATIVE FILTERING

  25. None
  26. - USER-USER - ITEM-ITEM - MATRIX FACTORIZATION approaches

  27. Song Alice Bob Carol Dave The Weeknd - Starboy 3

    5 Luis Fonsi - Despacito 1 5 Lorde - 400 Lux 1 2 4 5 Major Lazer - Light it Up 3 0 5 5 The White Stripes - Seven Nation Army 4 1 2 1 Red Hot Chili Peppers - Scar Tissue 5 2 0 Taylor Swift - Shake it Off 3 5 2
  28. Song Alice Bob Carol Dave The Weeknd - Starboy 3

    5 Luis Fonsi - Despacito 1 5 Lorde - 400 Lux 1 2 4 5 Major Lazer - Light it Up 3 0 5 5 The White Stripes - Seven Nation Army 4 1 2 1 Red Hot Chili Peppers - Scar Tissue 5 2 0 Taylor Swift - Shake it Off 3 5 2
  29. Song Alice Bob Carol Dave The Weeknd - Starboy 3

    5 Luis Fonsi - Despacito 1 5 Lorde - 400 Lux 1 2 4 5 Major Lazer - Light it Up 3 0 5 5 The White Stripes - Seven Nation Army 4 1 2 1 Red Hot Chili Peppers - Scar Tissue 5 2 0 Taylor Swift - Shake it Off 3 5 2
  30. - DOESN’T NEED TO UNDERSTAND THE DATA advantages

  31. - CANNOT RECOMMEND NEW ITEMS (THAT HAVEN’T BEEN RATED) -

    TENDS TO RECOMMEND POPULAR ITEMS disadvantages
  32. HYBRID SYSTEMS

  33. THINGS TO LOOK OUT FOR

  34. COLD START

  35. CONTEXT MATTERS

  36. RECENT ITEMS MATTER (MORE)

  37. EXPLICIT/IMPLICIT FEEDBACK

  38. QUESTIONS?

  39. THE END