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DiscoRank: optimizing discoverability on SoundCloud

These are the slides of the presentation I gave at the Realtime Conf EU on 23rd April 2013. The full abstract of the talk can be found here: http://lanyrd.com/2013/realtime-conf-europe/scdtyf/

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DiscoRank: Optimizing Discoverability
on SoundCloud
Amélie Anglade
• Developer at SoundCloud
• SoundCloud is the
world’s largest social
sound platform
• Academic background in
Music Information
Retrieval (MIR)
• Design, prototype and
implement Machine
Learning algorithms for
music discovery
DISCOVERABILITY ?
DiscoRank: optimizing discoverability on SoundCloud
DiscoRank: optimizing discoverability on SoundCloud
DiscoRank: optimizing discoverability on SoundCloud

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DiscoRank: optimizing discoverability on SoundCloud

  • 1. DiscoRank: Optimizing Discoverability on SoundCloud Amélie Anglade
  • 2. • Developer at SoundCloud • SoundCloud is the world’s largest social sound platform • Academic background in Music Information Retrieval (MIR) • Design, prototype and implement Machine Learning algorithms for music discovery
  • 8. • The web is a graph: • nodes = web pages • edges = hyperlinks • The (Page)rank of a node depends on the link structure of the graph WEB AND PAGERANK
  • 12. Nodes visited more often: • Nodes with many links • Coming from frequently visited nodes RANDOM SURFER A B C D E
  • 13. Adjacency matrix A COMPUTING THE PAGERANK A B C D E Transition probability matrix M Probability distribution of surfer’s position
  • 14. Adjacency matrix A COMPUTING THE PAGERANK A B C D E Transition probability matrix M Probability distribution of surfer’s position
  • 15. Adjacency matrix A COMPUTING THE PAGERANK A B C D E Transition probability matrix M Probability distribution of surfer’s position
  • 16. Adjacency matrix A COMPUTING THE PAGERANK A B C D E Transition probability matrix M Probability distribution of surfer’s position
  • 17. Adjacency matrix A COMPUTING THE PAGERANK A B C D E Transition probability matrix M Probability distribution of surfer’s position
  • 18. Adjacency matrix A COMPUTING THE PAGERANK A B C D E Transition probability matrix M Probability distribution of surfer’s position
  • 22. If N nodes in graph, probability to teleport to any other node (including self) = 1/N TELEPORT A B C D E 1/N 1/N 1/N 1/N 1/N
  • 23. TELEPORT A B C D E 1/N 1/N 1/N 1/N α ? 1-α 1/N At regular node: invoke teleport operation with probability α and standard random walk with probability (1 - α)
  • 24. Probability distribution of the surfer at any time is a vector. COMPUTING THE PAGERANK That vector converges to a steady state: the PageRank vector.
  • 29. • Search across People, Sounds, Sets, Groups • One unique rank vector that contains all entities • Weight the links based on the type of event: • User favorites Track • Track is featured in Playlist ... • New big (but sparse) adjacency matrix: UNIVERSAL SEARCH
  • 31. • How do we identify content that is trending? • The more recent a listen, favorite, etc. (event) the higher the weight • Multiply each event (=edge) by a time decay: • New adjacency matrix: BACK TO EXPLORE
  • 33. • Millions of entities(=nodes) and events(=edges) • First DiscoRank: several hours of computation • Trimmed down to a few minutes using: • Sparse matrix • Optimized storage of the graph in memory • Versioned copies of the DiscoRank • So technically we could compute the DiscoRank realtime A VERY LARGE GRAPH
  • 34. • • Re-mapping entity ids • Memory optimization so the graph holds in memory: • All edges details are stored in memory in a byte[] • buffer the byte[] into an opaque byte block pool • no object • sort the buffered byte[] in place • On disk and when computing the DiscoRank: • Delta encoded ordered adjacency lists: • One “from” node, several “to” nodes • Delta encode the “to” node ids USING SPARSITY
  • 35. • We keep versioned copies of: • the DiscoRank vector of results • the DiscoRank graph • We rebuild the entire DiscoRank graph from scratch once a week • In between: • we create additional graph segments with new entities and events • and use as prior for the DiscoRank computation the results of the previous DiscoRank run • Side effect: • Also allows for experimentation VERSIONED DISCORANK
  • 36. • MySQL batch jobs • DiscoRank results stored in HDFS • At the end of every DiscoRank run we re-load it in ElasticSearch: • For each item we combine its Lucene score with its DiscoRank INTEGRATION IN OUR INFRASTRUCTURE
  • 37. Amélie Anglade Sound/Music Information Retrieval Engineer about.me/utstikkar @utstikkar We’re hiring! www.soundcloud.com