1.
May 6th, 2014
Real-Time Personalization
with Ad Tech @Spotify
Kinshuk Mishra
kinshuk@spotify.com
@_kinshukmishra
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About Me
• @Spotify since 2011
• Data and Backend engineering
• With the Monetization team

3.
May 12, 2014
Spotify in numbers
Started in 2006, available in 56 markets
20+ million songs, 20,000 added daily
24+ million active users, 6+ million subscribers
1.5 billion playlists

4.
May 12, 2014
Monetization at Spotify
• $1 billion paid to rights holders since launch
• 1 in every 4 Spotify users is a paying subscriber
• Ad revenue pays for free tier music streaming
• Build platform to power artist and music label promotions

5.
What is our mission?
• Power the free tier with ads customers love.
– Delight users, brands and artists with standout
promotional experiences.
– Deliver the right message for every moment.
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Delight users, brands and artists with standout
promotional experience
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• Ad formats need to be
- Native
- Actionable
- Scalable
- Engaging

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Spotlight

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Album promotion

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Sponsored Genre

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Sponsored Genre

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!(Right message for every moment)

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Things we had to do
• Make ads context aware.
• Create measurable ad formats.
• Build tools to help brands understand their audience.
• Build flexible targeting infrastructure.
• Analyze user behavior to improve ad relevancy.
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Why is real time interesting?
• True assessment of the context
- current view, last track, etc.
• Immediate feedback
- ad clicked, followed a playlist, etc.
• Increased ad opportunities
- hyperlocal, current mood, etc.
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source : http://nativemobile.com/tag/real-time-bidding

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How to serve right message for every moment ?
• Fix the WTFs for ads in the music context
- Realtime short window activity analysis
• Improve ad relevancy based on user behavior
- (Realtime + Batch) long window activity analysis
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Some quick wins
• Fix the WTFs for ads in the music context
- Fix the “Not Safe For Kids” ads problem.
- Fix the relevancy of ads in your music session.
• Improve ad relevancy based on user behavior
- Fix ad relevancy based on user’s musical taste.
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Targeting Architecture
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Desired overall design
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Our requirements
• Scalable log collection
• Capability to process logs in batch and
realtime mode and aggregate user activity
• Capability to store user profiles and
enable serve time lookups
• Ability to update and add new features to
existing user profiles

19.
Bigdata @Spotify and choices
• 700 node hadoop cluster
• 400 GB service logs daily
• 4.5 TB user data daily
• 7500 hadoop jobs daily
• 64 TB data generated daily
• YARN Map-Reduce, Giraph,
Storm, Spark, etc.
!
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Overall design and tech choices
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Source : http://tfosuccess.com/day-106-107-the-storm-arrived/
What is Storm?
• Real time stream processing
• Like Hadoop without HDFS
• Like Map/Reduce with many reducer steps
• Fault tolerant and guaranteed message processing
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Storm @Spotify
• storm-0.8.0
• 22 node cluster
• 15+ topologies
• 200k tuples/second
• ads, recommendation,
analytics, monitoring, etc.
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source: http://storm.incubator.apache.org/

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Use-case
Use real-time session genre information to control
ad serving in real-time
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Realtime activity analysis
• Kafka -> Storm -> Memcached -> Targeting Backend
• Soft realtime
• Easy to scale
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Getting data
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Getting data across the globe
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Genre Topology
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May 12, 2014
Measuring effectiveness
• Build ad quality scores from lower-level metrics
• Positive response, avoidance, etc.
• A/B test hypotheses, evaluate against quality scores
• Build quality score optimization into real-time system

29.
May 6th, 2014
Want to join the band?
https://www.spotify.com/us/jobs/ or
https://twitter.com/Spotifyjobs
!
Kinshuk Mishra
kinshuk@spotify.com @_kinshukmishra