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

1. Last.fm’s Recommendation Engine
Erik Frey
Last.fm

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. 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. Recommendations: Lean Forward

5. Recommendations: Lean Back

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. The Recipe: Scrobbles

8. The Recipe: Tags
Ayla Nereo

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