Erik Frey on Collaborative Filters at SXSW

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Erik Frey of Last.fm presenting at "Collaborative Filters: The Evolution of Recommendation Engines" at SXSW Interactive, March 14 2009

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Erik Frey on Collaborative Filters at SXSW

  1. 1. Last.fm’s Recommendation Engine Erik Frey Last.fm
  2. 2. Last.fm:
About
 Listen
to
music
for
free
(supported
by
ads)
 •  Be
social:
25m
unique
users/month
 •  Radio,
videos,
events,
charts,
…
 •  Recommendations
 •  •  Founded
in
2002
 •  London

  3. 3. Recommendations:
Overview
 •  Some
relation
between
 –  Millions
of
songs
 –  Millions
of
users
 •  Last.fm
exposes
as:
 –  Music
recommendations
(artists,
albums,
tracks,
events,
videos)
 –  People
recommendations!

Find
your
musical
neighbors.
 –  Not
just
for
robots:
user‐to‐user
recommendations.
 •  Different
kinds
of
recommendations:
 –  Lean
forward
(e.g.
browsing
lists
of
similar
artists)
 –  Lean
back
(e.g.
personalized
radio)

  4. 4. Recommendations:
Lean
Forward

  5. 5. Recommendations:
Lean
Back

  6. 6. Recommendations:
The
Recipe
 •  Data
is
the
most
important
ingredient
 – Scrobbles
(listening
data)
 •  [user,
track,
timestamp]
 •  25
billion
+800
million
per
month
 •  No
deliberate
user
input
 – Social
Tags
 •  [user,
item,
tag]
 •  50
million
+2.5
million
per
month
 •  300k
unique
taggers
per
month
 – And
more:
love,
ban,
skip…

  7. 7. The
Recipe:
Scrobbles

  8. 8. The
Recipe:
Tags
 Ayla
Nereo

  9. 9. The
Recipe:
Two
Models
 User’s
 Scrobbles,
 attention
 tags,
…
 profile
 Model
of
 how
things
 Recs
 are
related


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