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Recommendations at SensCritique. Mixing social and machine learning

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Talk given by Xavier Rampino during the RecsysFR meetup on March 23rd 2016.

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Recommendations at SensCritique. Mixing social and machine learning

  1. 1. Recommendations at SensCritique Mixing social and machine learning By Xavier RAMPINO, Mars 2016
  2. 2. What is SensCritique ?
  3. 3. What is SensCritique ?
  4. 4. What is SensCritique ?
  5. 5. Entertainment database What is SensCritique ?
  6. 6. Movies, TV Shows, Video Games, Books, Comics, Music Entertainment database What is SensCritique ?
  7. 7. Movies, TV Shows, Video Games, Books, Comics, Music Entertainment database Virtual library What is SensCritique ?
  8. 8. Movies, TV Shows, Video Games, Books, Comics, Music Entertainment database Virtual library Rate, List, Review, add to a wish list What is SensCritique ?
  9. 9. Movies, TV Shows, Video Games, Books, Comics, Music Entertainment database Virtual library Social network Rate, List, Review, add to a wish list What is SensCritique ?
  10. 10. Movies, TV Shows, Video Games, Books, Comics, Music Entertainment database Virtual library Social network Rate, List, Review, add to a wish list Users can : Follow each other, like activities, recommend products What is SensCritique ?
  11. 11. Database + Library + Network What is SensCritique ?
  12. 12. Database + Library + Network What is SensCritique ? =
  13. 13. Database + Library + Network What is SensCritique ? =
  14. 14. What is SensCritique ? Database + Library + Network
  15. 15. What is SensCritique ? Database + Library + Network
  16. 16. What is SensCritique ? Database + Library + Network Discovery Engine
  17. 17. What is SensCritique ? Discovery tool Database + Library + Network Discovery Engine
  18. 18. What is SensCritique ? Discovery tool Discovery tool Database + Library + Network Discovery Engine
  19. 19. What is SensCritique ? Discovery tool Discovery tool Discovery tool Database + Library + Network Discovery Engine
  20. 20. What is SensCritique ? 14M 600K 55M 1,5M products registered users ratings lists
  21. 21. How we make it work
  22. 22. How we make it work
  23. 23. How we make it work • Multimedia Database Entertainment database
  24. 24. How we make it work • Multimedia Database • Extensive Cinema, TV, 
 and VOD showtimes Entertainment database
  25. 25. How we make it work • Multimedia Database • Extensive Cinema, TV, 
 and VOD showtimes • Graph Database Entertainment database
  26. 26. How we make it work Key factors for discovery : • Serendipity • Familiar cues (actors, franchises) • Familiar media (TV, cinema) Entertainment database
  27. 27. How we make it work Virtual Library We have very enthusiastic members
  28. 28. How we make it work Virtual Library Our past recommendation engine stack
  29. 29. How we make it work Virtual Library Our past recommendation engine stack Ratings Wish CSV dump LensKit MySql
  30. 30. How we make it work Virtual Library Our past recommendation engine stack Very extensive operation, once a day Ratings Wish CSV dump LensKit MySql
  31. 31. How we make it work Virtual Library Our current recommendation engine stack
  32. 32. How we make it work Virtual Library Our current recommendation engine stack Nginx Logs Web hooks prediction.io event server Application
  33. 33. How we make it work Virtual Library Our current recommendation engine stack Nginx Logs Web hooks prediction.io event server Application • Live Update • Take product views into account • Live boosting during querying (via Elasticsearch)
  34. 34. How we make it work Social Network We use social recommendations at two levels
  35. 35. How we make it work Social Network We use social recommendations at two levels User-User recommendation Live Feed Direct
  36. 36. How we make it work Social Network We use social recommendations at two levels User-User recommendation Live Feed Direct News filtering Tops Aggregated
  37. 37. How we make it work Once one has chosen people to follow, he gets live update on their entertainment activity Social Network Direct
  38. 38. How we make it work He may also be given direct recommendation Social Network Direct
  39. 39. How we make it work To allow one member to find similar members, we made a user-user recommendation engine using Mahout : Social Network Direct Online recommendation system, webservice served over Glassfish Event updates are sent on RabbitMQ, then dispatched to a Java Consumer
  40. 40. How we make it work One member can notice than a subgroup of member he follows did an activity on a specific product Social Network Aggregated
  41. 41. How we make it work Community choices, given an editorial theme Their own activity (we’ll show them only polls for which they already rated some of the result products) Social Network Aggregated We have also polls, in which all members can participate. Member then get recommendations fuelled by two forces :
  42. 42. How we make it work Social Network Aggregated
  43. 43. Conclusion
  44. 44. From its origins, SensCritique is headed towards product discovery, as a whole though 3 ways : • Database : We tend to have a comprehensive and ambitious graph-oriented database • Library : We are a tool for the user to keep a memory of his entertainment history • Social : We foster social activity between our members, and we are proud of our community We strongly believe that this is thanks to these three factors that we can offer both a tailored and a global recommendation offer to our audience.
  45. 45. Questions ? Question No question

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