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Music discovery: What, why, who, when, where?

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Recommending music is promising that you will make people like, feel or remember something when they’ll listen. What is pushing you to get adventurous and hit “play”?

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Music discovery: What, why, who, when, where?

  1. 1. Music discovery: What, why, who, when, where? Julie Knibbe Senior Product Manager, Discovery Deezer @julieknibbe
  2. 2. - - The tricky part of the problem. What? Music
  3. 3. Music is Personal We associate music with people, emotions, memories... ● Recommending music is promising that you will make people like, feel or remember something when they’ll listen. ● Failing at recommending something right is nothing less than an insult or a disappointment for users. ● And most of the time they’ll take it personally. ● When Amazon recommends the wrong hairdryer, it’s not so bad.
  4. 4. - - Why would you want to discover music? Music discovery is WORK. Why?
  5. 5. Triggers - Identity: Music is your identity, listening to a genre makes you feel like you belong to a community - Social status: You value being the one the others turn to when they want something new - Fear Of Missing Out: You don’t want to be the last one finding out about Major Lazer - Boredom: You’re tired of listening the same old songs. You need to feel something and be alive. - Fear of Loneliness/Distraction: Hearing noise, especially human voices, is comforting What is pushing you to get adventurous and hit “play”?
  6. 6. When? Where? Music discovery requires a bit of time, and maybe headphones.
  7. 7. Passive discovery - Listening to radio while shopping - Shazaming during a party - Get recommendations from friends when you meet them - … When music comes to you • Slipping in your music bubble while commuting • Getting ready in the morning • Take a break at work to escape for a minute • …. When you go after it Active discovery
  8. 8. Is it really about me? Message Content in Social Media Streams http://infolab.stanford.edu/~mor/research/naamanCSCW10.pdf « On average, people spend 60% of conversations talking about themselves – and this figure jumps to 80% when communicating via social media platforms such as Twitter or Facebook. » It’s about you. A personal process. And we’re all at least a little self-involved.
  9. 9. Who?
  10. 10. /01 /02 /03 /04 Self directed expert Curator Curious wanderer Guided listener We are not all investing the same amount of time in music though. Inspired by: Understanding users of commercial music services through personas; design implications http://ismir2015.uma.es/articles/12_Paper.pdf
  11. 11. Preferred tools: - Search - Own playlist curation Investment: +++ Guidance openness: --- Trust in algorithms: --- Self Directed Expert Triggers / Drivers: - Build identity - Keep « trendsetter » social status - Get recognized / go to person - Fear of missing out - Share/show off tastes « When I listen to the radio, it’s KEXP, and it’s usually a really short amount of time in the morning. I know what I want to listen to. » « Pandora (…) they’re missing out on something and I don’t know what it would be called, like context, and how the music makes me feel. » « I do my own ways of [finding], and I rely on my friends and people I write with to recommend stuff. »
  12. 12. Preferred tools: - Search / Advanced search - Own playlist curation - Similar Artists - Channels Investment: +++ Guidance openness: 0 Trust in algorithms: ++ Curator Triggers/Drivers : - Learn something new - Learn something about me - Understand how things work and how they’re linked - Share knowledge « I would love to see the metadata that goes into choosing each song… I’d love to be able to pick and choose those attributes, so I could say, ‘ok, I do like those smooth jazz elements, but I don’t like the saxophone solos.’ » « I’m looking for linkages from music to music. »
  13. 13. Preferred tools: • Charts • Weekly Recommendations • Radios • Curated playlists • Similar Artists • Channels Investment: + Guidance openness: + Trust in systems: ++ Curious wanderer Triggers / Drivers: - Stay up to date - Get entertained - Daydream, escape real life - Escape boredom « .. When it recommends me things that I never would have thought of, so I think, ‘yeah, I’ll give it a shot’ » « The serendipity of finding new music is what I enjoy most. Generally if I’m listening to new music it will be because a friend recommended it or I came across it on Youtube.. I listen to pretty diverse things. »
  14. 14. Preferred tools: - Radios - Mood playlists - Moment recommendations - Charts Investment: ---- Guidance openness: +++ Trust in systems: +++ Guided listener Triggers / Drivers: - Find ambiance/music for activities (sports, dinner, ..) or mood (breakup, party) - Isolate to focus - Relax - Morning / commuting routine - Drive out boredom « I mean, I can get this thing booted up and going within seconds, and then I’m off doing dishes or whatever, which contributes to my satisfaction. It’ s going to do what I want it do to immediately. Boom. Off I go. »
  15. 15. - - Wrong question. Humans or Algorithms?
  16. 16. - - TRUSTING ALGORITHMS? When we don’t trust algorithms, and when we do « Algorithm avoidance »: people prefer human judgment, and as a result often make worse decisions Mistakes are held against algorithms more than against a human being, under the (false) assumption that human judgment can improve while an algorithm can’t. To increase confidence in an algorithm, people need to feel more that they (= humans) are in control: - Understand why an algorithm predicts a result - Tweak the results, give feedback https://hbr.org/2015/06/when-your-boss-wears-metal-pants
  17. 17. Continuously adapting to constant changes THE RECOMMENDATION CYCLE Change of tastes, time, location, and context in general lead to various needs: 1. Get to know your user: Keep learning about his/her preferences 2. Build trust by showing your analytics 3. Advise 4. Learn from feedback: analyze why a recommendation failed and learn from that mistake Get to know you Build trust – how well do I know you? Advise you Learn from feedback

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