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Help! My iPod thinks I'm emo.
 

Help! My iPod thinks I'm emo.

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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 ...

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.

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  • there is nothing wrong with likeing scremo metal core stuff im emo and proud of it
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Help! My iPod thinks I'm emo. Help! My iPod thinks I'm emo. Presentation Transcript

  • 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
  • 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
  • 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
  • Why do we care? Compulsory Long Tail slide Monday, March 23, 2009
  • Why do we care? Compulsory Long Tail slide Monday, March 23, 2009
  • Why do we care? Compulsory Long Tail slide Monday, March 23, 2009
  • Why do we care? Compulsory Long Tail slide Monday, March 23, 2009
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Help! My iPod thinks I’m emo Why is music recommendation broken? Monday, March 23, 2009
  • 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 Monday, March 23, 2009
  • 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
  • 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
  • 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. Monday, March 23, 2009
  • The Harry Potter Problem If you like X you might like Harry Potter Monday, March 23, 2009
  • The Harry Potter Problem If you like X you might like Harry Potter Monday, March 23, 2009
  • 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
  • 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
  • The Novelty Problem If you like The Beatles you might like ... Monday, March 23, 2009
  • The Napoleon Dynamite Problem Some items are not easy to categorize Monday, March 23, 2009
  • The Opacity Problem “If you like NiN you might like Johnny Cash” - why? Why??? Monday, March 23, 2009
  • Hacking the recommender Recommenders can be easily manipulated for fun or profit Monday, March 23, 2009
  • Hacking the recommender Recommenders can be easily manipulated for fun or profit Monday, March 23, 2009
  • Help! My iPod thinks I’m emo Fixing music recommendation Monday, March 23, 2009
  • 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
  • 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 Monday, March 23, 2009
  • 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
  • Fixing music recommendation Content-based recommendation 4 x 10 2 1 0 ! -1 ! -2 0 5 10 15 20 25 sec. BPM 190 143 114 96 72 60 0 5 10 15 20 25 sec. 1 0.8 0.6 0.4 0.2 0 60 72 96 114 143 BPM 190 240 Monday, March 23, 2009
  • 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
  • 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
  • What if? What if technology isn’t the answer? No magic recommender? photo by mebajason (cc) Monday, March 23, 2009
  • 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
  • Not all listeners equal Different types of listeners need different kinds of recommenders Monday, March 23, 2009
  • Not all listeners equal Different types of listeners need different kinds of recommenders Indifferents 40% Monday, March 23, 2009
  • Not all listeners equal Different types of listeners need different kinds of recommenders Casuals 32% Indifferents 40% Monday, March 23, 2009
  • Not all listeners equal Different types of listeners need different kinds of recommenders Enthusiasts 21% Casuals 32% Indifferents 40% Monday, March 23, 2009
  • 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
  • Accept irrationality To make real discoveries possible: Offer unique presentation Show context Create meaning Monday, March 23, 2009
  • Unique Presentation & Editorial Ishkurs’ guide to Electronic Music (http://techno.org) Monday, March 23, 2009
  • Context & Interaction Guitar hero / Rock Band gameplay Monday, March 23, 2009
  • Context What’s on her playlist? imeem (http://imeem.com), social playlists Monday, March 23, 2009
  • Context & Meaning via Editorial 14 Tracks: Weekly themed selections (http://14tracks.com) 14 Tracks that make you wish you played the piano: Monday, March 23, 2009
  • Playful Presentation & Meaning thesixtyone (http://thesixtyone.com) Monday, March 23, 2009
  • Visible Context & Meaning The Hype Machine Monday, March 23, 2009
  • Meaning via Authoritative Editorial Pitchfork’s Best New Music pages (http://pitchforkmedia.com) Monday, March 23, 2009
  • The Future of Music Discovery Listen, Learn, Understand, Engage - for all music. Monday, March 23, 2009
  • The Future of Music Discovery Listen, Learn, Understand, Engage - for all music. Monday, March 23, 2009
  • The Future of Music Discovery Listen, Learn, Understand, Engage - for all music. Monday, March 23, 2009
  • The Future of Music Discovery Listen, Learn, Understand, Engage - for all music. Monday, March 23, 2009
  • 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
  • The Future of Music Discovery Editors, people, serendipity. Scaled. image by luc legay (CC) Monday, March 23, 2009
  • 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