• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Auralist: Introducing Serendipity into Music Recommendation
 

Auralist: Introducing Serendipity into Music Recommendation

on

  • 1,131 views

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.

Statistics

Views

Total Views
1,131
Views on SlideShare
1,115
Embed Views
16

Actions

Likes
3
Downloads
22
Comments
0

4 Embeds 16

http://www.cl.cam.ac.uk 13
http://a0.twimg.com 1
http://profzero.org 1
http://researchswinger.org 1

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • notes
  • notes
  • notes
  • notes
  • notes
  • notes

Auralist: Introducing Serendipity into Music Recommendation Auralist: Introducing Serendipity into Music Recommendation Presentation Transcript

  • 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
    • Goal: how to produce recommendations that are
    • Accurate
    • Diverse
    • Novel
    • Serendipitous
    • Auralist: framework broadening musical horizons ;)
    • Basic
    • Community-Aware
    • Bubble-Aware
    • Full
    • Basic
    • Employs Latent Dirichlet Allocation (LDA)
  • 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