Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better.
Slides from the SXSW 2009 Panel. Speakers: Paul Lamere from The Echo Nest, and Anthony Volodkin from The Hype Machine.
1. Help! My iPod
thinks I’m emo.
SXSW Interactive
March 17, 2009
#sxswemo
Paul Lamere
Anthony Volodkin Photo (CC) by Jason Rogers
Monday, March 23, 2009
2. Music recommendation is broken
A recommendation that no human would make
If you like Britney Spears ...
You might like the Report on
Pre-War Intelligence
Monday, March 23, 2009
3. Help! My iPod thinks I’m emo
What we are talking about today
Important?
Broken?
Can we fix it?
Q/A
Monday, March 23, 2009
4. Why do we care?
Compulsory Long Tail slide
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5. Why do we care?
Compulsory Long Tail slide
Monday, March 23, 2009
6. Why do we care?
Compulsory Long Tail slide
Monday, March 23, 2009
7. Why do we care?
Compulsory Long Tail slide
Monday, March 23, 2009
8. State of music discovery
We can’t seem to find the long tail
Sales data for 2007
- 4 million unique tracks sold
But ...
- 1% of tracks account for 80% of sales
- 13% of sales are from American Idol or Disney artists
State of the Industry 2007 - Nielsen Soundscan
Monday, March 23, 2009
9. State of music discovery
We can’t seem to find the long tail
Sales data for 2007
- 4 million unique tracks sold
But ...
- 1% of tracks account for 80% of sales
- 13% of sales are from American Idol or Disney artists
State of the Industry 2007 - Nielsen Soundscan
Monday, March 23, 2009
10. State of music discovery
We can’t seem to find the long tail
Sales data for 2007
- 4 million unique tracks sold
But ...
- 1% of tracks account for 80% of sales
- 13% of sales are from American Idol or Disney artists
State of the Industry 2007 - Nielsen Soundscan
Make everything available
Monday, March 23, 2009
11. State of music discovery
We can’t seem to find the long tail
Sales data for 2007
- 4 million unique tracks sold
But ...
- 1% of tracks account for 80% of sales
- 13% of sales are from American Idol or Disney artists
State of the Industry 2007 - Nielsen Soundscan
Make everything available
Help me find it
Monday, March 23, 2009
12. Help! I’m stuck in the head
The limited reach of music recommendation
Popularity
83 Artists 6,659 Artists 239,798 Artists
Sales Rank
Study by Dr. Oscar Celma - MTG UPF
Monday, March 23, 2009
13. Help! I’m stuck in the head
The limited reach of music recommendation
48% of recommendations
Popularity
83 Artists 6,659 Artists 239,798 Artists
Sales Rank
Study by Dr. Oscar Celma - MTG UPF
Monday, March 23, 2009
14. Help! I’m stuck in the head
The limited reach of music recommendation
48% of recommendations
Popularity
52% of recommendations
83 Artists 6,659 Artists 239,798 Artists
Sales Rank
Study by Dr. Oscar Celma - MTG UPF
Monday, March 23, 2009
15. Help! I’m stuck in the head
The limited reach of music recommendation
48% of recommendations
Popularity
0% of
recommendations
52% of recommendations
83 Artists 6,659 Artists 239,798 Artists
Sales Rank
Study by Dr. Oscar Celma - MTG UPF
Monday, March 23, 2009
16. Help! My iPod thinks I’m emo
Why is music recommendation broken?
Monday, March 23, 2009
17. The Wisdom of Crowds
How does collaborative filtering work?
35% 4% 62% 8% 60%
Overlap Data based on listening behavior of 12,000 Last.fm Listeners
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18. The stupidity of solitude
The Cold Start problem
If you like Blondie, you might like the DeBretts ...
But the recommender will never tell you that.
Monday, March 23, 2009
19. The stupidity of solitude
The Cold Start problem
If you like Blondie, you might like the DeBretts ...
But the recommender will never tell you that.
Monday, March 23, 2009
20. The stupidity of solitude
The Cold Start problem
If you like Blondie, you might like the DeBretts ...
0%
0%
0%
But the recommender will never tell you that.
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21. The Harry Potter Problem
If you like X you might like Harry Potter
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22. The Harry Potter Problem
If you like X you might like Harry Potter
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23. Popularity Bias
Rich get richer - diversity is the biggest loser
Results of popularity bias:
- Rich get richer
- Loss of diversity
- No long tail recommendations
Monday, March 23, 2009
24. Popularity Bias
Rich get richer - diversity is the biggest loser
Results of popularity bias:
- Rich get richer
- Loss of diversity
- No long tail recommendations
Monday, March 23, 2009
25. The Novelty Problem
If you like The Beatles you might like ...
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26. The Napoleon Dynamite Problem
Some items are not easy to categorize
Monday, March 23, 2009
27. The Opacity Problem
“If you like NiN you might like Johnny Cash” - why?
Why???
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28. Hacking the recommender
Recommenders can be easily manipulated for fun or profit
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29. Hacking the recommender
Recommenders can be easily manipulated for fun or profit
Monday, March 23, 2009
30. Help! My iPod thinks I’m emo
Fixing music recommendation
Monday, March 23, 2009
31. Fixing music recommendation
Eliminating popularity bias and feedback loops
Tim Eric Jim Brian
Adam Paul
Aaron Peter Chris Liz Yury Todd
Bethe Kirk Erik Tristan Sue Jen Todd
Jason
Scotty Erik
Cari Jason
Monday, March 23, 2009
32. Fixing music recommendation
Semantic-based recommendation
pop legend dance diva sexy
american guilty pleasure
teen pop 00s soul
90s pop rnb
dance-pop
rock rock dance-pop soul
singer-songwriter
hot 90s
emo alternative
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33. Fixing music recommendation
Where does this information come from?
Playlists
Artist Bios
The web
Reviews
Blogs
Lyrics
pop legend dance diva Forums
sexy american guilty Events
pleasure 90s teen pop 00s
rnb pop rock rock singer-
songwriter dance-pop soul emo Social sites
hot 90s
alternative
female fronted metal dark
rock alternative goth metal metal
goth rock emo gothic dark
heavy metal gothic
gothic rock
metal hard rock melodic metal
Crawler
symphonic metal rock metal
pop nu
Monday, March 23, 2009
35. Content-based recommendation
Using machines to listen to music
Perceptual features audio: Pattern layer
- time signature / tempo linear ratio
square ratio
- key/ mode Beat layer
1:4
- timbre 1:16
- pitch Segment layer ~1:3.5
- loudness ~1:12.25
- structure ~1:40
Frame layer ~1:1600
1:512
1:262144
Audio layer
4
x 10
2
1
wave form
0
! -1
! -2
0 0.5 1 1.5 2 2.5 3 sec.
25
25-band spectrogram
20
15
10
5
1
0 0.5 1 1.5 2 2.5 3 sec.
1
0.8
Monday, March 23, 2009
ss
0.6
36. Hybrid Recommendation
The best of all worlds
listener data Hybrid Recommender
preference editorial
user history
35% 4% 62% 8% 60%
recommendations
12% 18% 17% 34% 21%
audio data 4% 49% 5% 48% 7%
artist
collaborative filterer
audio
analysis
track
web data
content-based
user
adj Term K-L bits np Term K-L bits
random guessing is:
aggressive 0.0034 reverb 0.0064
softer 0.0030 the noise 0.0051
synthetic 0.0029 new wave 0.0039
a Na
KL = log punk 0.0024 elvis costello 0.0036
(a + b) (a + c)
N sleepy 0.0022 the mud 0.0032
cultural
funky 0.0020 his guitar 0.0029
b Nb
+ log noisy 0.0020 guitar bass and drums 0.0027
(a + b) (b + d)
N angular 0.0016 instrumentals 0.0021
analysis
c Nc acoustic 0.0015 melancholy 0.0020
+ log romantic 0.0014 three chords 0.0019
(a + c) (c + d)
N
d Nd Table 2. Selected top-performing models of adjective and
+ log
(b + d) (c + d)
semantic-based
N noun phrase terms used to predict new reviews of music
with their corresponding bits of information from the K-L
(3)
distance measure.
This measures the distance of the classifier away from a
degenerate distribution; we note that it is also the mu-
7.4. Review Regularization
tual information (in bits, if the logs are taken in base 2)
Monday, March 23, 2009
37. What if?
What if technology isn’t the answer?
No magic recommender?
photo by mebajason (cc)
Monday, March 23, 2009
38. Taste is irrational
What makes people like things?*
Social connections:
Who else likes this? Are they _____?
How many of them are there?
Context:
Purpose / Surroundings
Medium, related info, meaning
* Most won’t admit this if you ask
Monday, March 23, 2009
39. Not all listeners equal
Different types of listeners need different kinds of recommenders
Monday, March 23, 2009
40. Not all listeners equal
Different types of listeners need different kinds of recommenders
Indifferents 40%
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41. Not all listeners equal
Different types of listeners need different kinds of recommenders
Casuals 32%
Indifferents 40%
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42. Not all listeners equal
Different types of listeners need different kinds of recommenders
Enthusiasts 21%
Casuals 32%
Indifferents 40%
Monday, March 23, 2009
43. Not all listeners equal
Different types of listeners need different kinds of recommenders
Savants 7 %
Enthusiasts 21%
Casuals 32%
Indifferents 40%
Monday, March 23, 2009
44. Accept irrationality
To make real discoveries possible:
Offer unique presentation
Show context
Create meaning
Monday, March 23, 2009
45. Unique Presentation & Editorial
Ishkurs’ guide to Electronic Music (http://techno.org)
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47. Context
What’s on her playlist? imeem (http://imeem.com), social playlists
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48. Context & Meaning via Editorial
14 Tracks: Weekly themed selections (http://14tracks.com)
14 Tracks that make you wish you played the piano:
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49. Playful Presentation & Meaning
thesixtyone (http://thesixtyone.com)
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51. Meaning via Authoritative Editorial
Pitchfork’s Best New Music pages (http://pitchforkmedia.com)
Monday, March 23, 2009
52. The Future of Music Discovery
Listen, Learn, Understand, Engage - for all music.
Monday, March 23, 2009
53. The Future of Music Discovery
Listen, Learn, Understand, Engage - for all music.
Monday, March 23, 2009
54. The Future of Music Discovery
Listen, Learn, Understand, Engage - for all music.
Monday, March 23, 2009
55. The Future of Music Discovery
Listen, Learn, Understand, Engage - for all music.
Monday, March 23, 2009
56. The Future of Music Discovery
The challenge
Music Music
I Like You Like
Music
I Used
To Like
Get this t-shirt at dieselsweeties.com
Monday, March 23, 2009
57. The Future of Music Discovery
Editors, people, serendipity. Scaled.
image by luc legay (CC)
Monday, March 23, 2009
58. Help! My iPod
thinks I’m emo.
Questions?
Paul Lamere
the.echonest.com
Paul@echonest.com
MusicMachinery.com
Anthony Volodkin
hypem.com
anthony@hypem.com
fascinated.fm
Photo (CC) by Jason Rogers
Monday, March 23, 2009