Auralist: Introducing Serendipity into Music Recommendation @danielequercia
Auralist: Introducing Serendipity into Music Recommendation
Jun 21, 2015
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
@danielequercia
<who am i>
daniele quercia
offline & online
Introducing serendipity in recommendations
Introducing serendipity in recommendations
Filter bubble (chilling idea … for some)
Your content limited by your past& self-propagating interests
Goal: how to produce recommendations that are
1.Accurate2.Diverse3.Novel4.Serendipitous
Auralist: framework broadening musical horizons ;)
1.Basic2.Community-Aware3.Bubble-Aware4.Full
1. 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 ov
er HolyBlood
2. Community-Aware
balance * match(user’s history, artist) * diversity(artist)
favors artists with
broader fan bases
e.g., The Beatles ov
er 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 artis
ts by
pushing the bo
undaries of a
user’s taste
4. Full Auralist
Rank interpolation of 1. 2. and 3.
do they work?
++ - - -
+- -+
+
Both improve novelty, diversity and serendipity but 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 serendipityMajority: favours of greater accuracy
* serendipity IS a user-specific parameter
So what?
Future (well, current & you could help)
1. Nudging
Now: AuralistNext: ‘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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