Mind Reading - Understanding Recommender Systems

Transcript

1. MIND READING - UNDERSTANDING
   RECOMMENDER SYSTEMS
   VLAD ILIESCU @
   

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2. A VERY IMPORTANT
   QUESTION™
   

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3. WHAT SUPER-POWER
   WOULD YOU LIKE?
   

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4. MIND READING IS NICE
   THIS TIME OF YEAR
   

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5. OFFLINE, IT’S PERSONAL
   

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6. ONLINE, IT DOESN’T SCALE
   

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

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

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9. REMEMBER NETFLIX’
   3-YEAR COMPETITION
   

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10. WHAT ARE
    RECOMMENDER
    SYSTEMS ANYWAY?
    

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11. THERE’S TWO TYPES OF
    RECOMMENDER SYSTEMS
    

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12. CONTENT-BASED FILTERING
    1.
    

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13. COLLABORATIVE FILTERING
    2.
    

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14. OKAY, MAYBE THERE’S
    THREE
    

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15. HYBRID SYSTEMS
    3.
    

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16. LET’S GO INTO A BIT
    MORE DETAIL
    

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17. CONTENT-BASED FILTERING
    

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18. - Gender
    - Age
    - Preferred genres
    - Artist
    - Year
    - Genre
    - Song Theme (extracted)
    

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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
    

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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
    

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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
    

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22. - DOESN’T DEPEND ON OTHER USERS
    - RECOMMEND UNPOPULAR ITEMS
    - COLD START ISSUES ARE LESS LIKELY
    advantages
    

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23. - OVERSPECIALIZATION
    - DIFFICULTY EXTRACTING ATTRIBUTES
    FROM RICH MEDIA (IMAGES, AUDIO, MOVIES)
    disadvantages
    

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24. COLLABORATIVE FILTERING
    

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26. - USER-USER
    - ITEM-ITEM
    - MATRIX FACTORIZATION
    approaches
    

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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
    

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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
    

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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
    

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30. - DOESN’T NEED TO UNDERSTAND THE DATA
    advantages
    

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31. - CANNOT RECOMMEND NEW ITEMS
    (THAT HAVEN’T BEEN RATED)
    - TENDS TO RECOMMEND POPULAR ITEMS
    disadvantages
    

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32. HYBRID SYSTEMS
    

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33. THINGS TO LOOK OUT FOR
    

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34. COLD START
    

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35. CONTEXT MATTERS
    

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36. RECENT ITEMS MATTER (MORE)
    

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37. EXPLICIT/IMPLICIT
    FEEDBACK
    

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38. QUESTIONS?
    

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39. THE END
    

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