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Algorithmic, Curated & Social Music Discovery

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As the Internet has made millions of tracks available for instant listening, digital music and streaming companies have focused on music recommendations and discovery.

Approaches have included using algorithms to present music tailored to listeners' tastes, using the social graph to find music, and presenting curated & editorial content.

This panel will discuss the methods, successes and drawbacks of each of these approaches. We will also discuss the possibility of combining all three approaches to present listeners with a better music discovery experience.

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Algorithmic, Curated & Social Music Discovery

  1. 1. juli 26, 2013 Algorithms, Curation, & Social Recommendations Music Discovery
  2. 2. What does a personalized music experience look like? Larry Page: “The perfect search engine would understand exactly what you mean and give back exactly what you want.” Our Mission: “The perfect music for every moment.” 2
  3. 3. Algorithmic Recommendations Core idea: If a group of listeners has a lot of overlap in the tracks, albums, or artists they listen to, that music is probably similar. We can recommend music from that collection to users in that group. We can also use other factors as input to algorithms: • Location • Age • Listening history & feedback • Other criteria Q: How can algorithm-driven recommendations power music discovery? What are the drawbacks, if any? 3
  4. 4. Algorithmic Recommendations 4
  5. 5. Curation & Editorial Content Expert curation can provide the human touch. Nick Holmstén, Tunigo: "The whole mission of our music application is to just have a play button. That's where we are starting from.” 5
  6. 6. Using the Social Graph 6
  7. 7. Discussion & Questions 7

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