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.

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

10. o ffline & online

11. Introducing serendipity in recommendations

12. Introducing serendipity in recommendations

16. F ilter bubble (chilling idea … for some) Your content limited by your past& self-propagating interests

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?

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