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Auralist: Introducing Serendipity into Music Recommendation  @danielequercia
U   C   L <who am i>
U   C   L daniele quercia
U   C   L
U   C   L
U   C   L
U   C   L
 
 
o ffline & online
Introducing serendipity in recommendations
Introducing serendipity in recommendations
 
 
 
F ilter bubble  (chilling idea  … for some) Your content limited by your past& self-propagating interests
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) Facebook  Twitter
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) Facebook  Twitter social econometrics
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) Facebook  Twitter social econometrics
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) For each doc:
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) For each doc:  users user1, user2, … (who belong to a given community)  artist
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) The Beatles HolyBlood
LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) The Beatles HolyBlood diversity() diversity() similarity()
1. Basic Auralist match( user’s history, artist )
2. Community-Aware balance  *  match( user’s history, artist ) *  diversity( artist )
2. Community-Aware balance  *  match( user’s history, artist ) *  diversity( artist ) favors  artists with  broader fan bases e.g., The Beatles over HolyBlood
2. Community-Aware balance  *  match( user’s history, artist ) *  diversity( artist ) favors  artists with  broader fan bases e.g., The Beatles over HolyBlood  …  but discounting for  popularity
3. Bubble-Aware The Beatles HolyBlood The Rolling Stones balance  *  match( user’s history, artist ) *  bubbleness( artist )
3. Bubble-Aware balance  *  match( user’s history, artist ) *  bubbleness( artist ) favors  cluster-avoiding  artists by pushing the boundaries of a user’s taste
4. Full Auralist Rank interpolation of 1. 2. and 3.
d o they work?
 
+ + - - -
+ - - + +
Both  improve  novelty, diversity and serendipity  b ut with accuracy loss  OK news!
Good news:  accuracy loss can be minimised good bad
Good news:  accuracy loss can be minimised good bad
User Study:  Basic Auralist vs. Full Auralist Serendipity Enjoyment
User Study:  Basic Auralist vs. Full Auralist Some:  accept accuracy loss for serendipity Majority:  favours of greater accuracy  *  serendipity IS a user-specific parameter
So what?
Future  (well, current & you could help)
1.  Nudging Now:  Auralist Next:  ‘Nudge’ people for serendipity
social media  language personality social media 2.  Personality
language personality social media 2.  Personality @ CSCW
3.  Why’s
2  personality 1   nudging 2  why’s
2  personality 1   nudging 2  why’s
@danielequercia

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Auralist: Introducing Serendipity into Music Recommendation

  • 1. Auralist: Introducing Serendipity into Music Recommendation @danielequercia
  • 2. U C L <who am i>
  • 3. U C L daniele quercia
  • 4. U C L
  • 5. U C L
  • 6. U C L
  • 7. U C L
  • 8.  
  • 9.  
  • 10. o ffline & online
  • 11. Introducing serendipity in recommendations
  • 12. Introducing serendipity in recommendations
  • 13.  
  • 14.  
  • 15.  
  • 16. F ilter bubble (chilling idea … for some) Your content limited by your past& self-propagating interests
  • 17.
  • 18.
  • 19.
  • 20. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) Facebook Twitter
  • 21. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) Facebook Twitter social econometrics
  • 22. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) Facebook Twitter social econometrics
  • 23. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) For each doc:
  • 24. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) For each doc: users user1, user2, … (who belong to a given community) artist
  • 25. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) The Beatles HolyBlood
  • 26. LDA create virtual bins (latent topics) assign words to a bin (@ random) for each bin: select pair of words if co-occur more than chance: keep them in the bin else: put them into another bin (@ random) The Beatles HolyBlood diversity() diversity() similarity()
  • 27. 1. Basic Auralist match( user’s history, artist )
  • 28. 2. Community-Aware balance * match( user’s history, artist ) * diversity( artist )
  • 29. 2. Community-Aware balance * match( user’s history, artist ) * diversity( artist ) favors artists with broader fan bases e.g., The Beatles over HolyBlood
  • 30. 2. Community-Aware balance * match( user’s history, artist ) * diversity( artist ) favors artists with broader fan bases e.g., The Beatles over HolyBlood … but discounting for popularity
  • 31. 3. Bubble-Aware The Beatles HolyBlood The Rolling Stones balance * match( user’s history, artist ) * bubbleness( artist )
  • 32. 3. Bubble-Aware balance * match( user’s history, artist ) * bubbleness( artist ) favors cluster-avoiding artists by pushing the boundaries of a user’s taste
  • 33. 4. Full Auralist Rank interpolation of 1. 2. and 3.
  • 34. d o they work?
  • 35.  
  • 36. + + - - -
  • 37. + - - + +
  • 38. Both improve novelty, diversity and serendipity b ut with accuracy loss OK news!
  • 39. Good news: accuracy loss can be minimised good bad
  • 40. Good news: accuracy loss can be minimised good bad
  • 41. User Study: Basic Auralist vs. Full Auralist Serendipity Enjoyment
  • 42. User Study: Basic Auralist vs. Full Auralist Some: accept accuracy loss for serendipity Majority: favours of greater accuracy * serendipity IS a user-specific parameter
  • 44. Future (well, current & you could help)
  • 45. 1. Nudging Now: Auralist Next: ‘Nudge’ people for serendipity
  • 46. social media language personality social media 2. Personality
  • 47. language personality social media 2. Personality @ CSCW
  • 49. 2 personality 1 nudging 2 why’s
  • 50. 2 personality 1 nudging 2 why’s

Notes de l'éditeur

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