Auralist: Introducing Serendipity into Music Recommendation
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Auralist: Introducing Serendipity into Music Recommendation

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Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised ...

Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that - in contrast to previous work - attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of ‘serendipitous discovery’, we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist’s emphasis on serendipity indeed improves user satisfaction.

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Auralist: Introducing Serendipity into Music Recommendation Presentation Transcript

  • 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.
    • Goal: how to produce recommendations that are
    • Accurate
    • Diverse
    • Novel
    • Serendipitous
  • 18.
    • Auralist: framework broadening musical horizons ;)
    • Basic
    • Community-Aware
    • Bubble-Aware
    • Full
  • 19.
    • Basic
    • Employs Latent Dirichlet Allocation (LDA)
  • 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
  • 43. So what?
  • 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
  • 48. 3. Why’s
  • 49. 2 personality 1 nudging 2 why’s
  • 50. 2 personality 1 nudging 2 why’s
  • 51. @danielequercia