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”?

1. Music discovery:
What, why, who, when,
where?
Julie Knibbe
Senior Product Manager, Discovery
Deezer
@julieknibbe

2. - -
The tricky part of the problem.
What? Music

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. - -
Why would you want to discover music? Music
discovery is WORK.
Why?

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. When?
Where?
Music discovery requires a bit of time, and
maybe headphones.

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. 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. Who?

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. 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. 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. 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. 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. - -
Wrong question.
Humans or
Algorithms?

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. 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