Auralist: Introducing Serendipity into Music Recommendation

Auralist: Introducing Serendipity into Music Recommendation @danielequercia

U C L <who am i>

U C L daniele quercia

o ffline & online

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) 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

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

@danielequercia

http://www.cl.cam.ac.uk	13
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Auralist: Introducing Serendipity into Music Recommendation

by Daniele Quercia on Feb 09, 2012

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