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How we built a "local popularity" recommender

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Robbert Van Der Pluijm, Bibblio Labs
Talk given during the 6th RecSysFR meetup on June 27th 2017.

Published in: Science
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How we built a "local popularity" recommender

  1. 1. RECSYS FRANCE #RecSysFR @Bibblio_org @RVanDerPluijm
  2. 2. WEBSITE(S) END USERSWIDGET RECOMMENDER A API ENDPOINT A RECOMMENDER B API ENDPOINT B RECOMMENDER C API ENDPOINT C ONLINE PUBLISHER, LIBRARY OR COURSE PLATFORM
  3. 3. WEBSITE(S) WIDGET END USERS ONLINE PUBLISHER, LIBRARY OR COURSE PLATFORM LOCAL POPULARITY API ENDPOINT WHY A LOCAL POPULARITY RECOMMENDER? Availability of interaction data Low complexity Taking another look at popularity
  4. 4. WEBSITE(S) WIDGET END USERS ONLINE PUBLISHER, LIBRARY OR COURSE PLATFORM LOCAL POPULARITY API ENDPOINT WHAT ARE THE CHALLENGES? Choosing the metric Scale and sparsity Not being greedy
  5. 5. WEBSITE(S) WIDGET END USERS ONLINE PUBLISHER, LIBRARY OR COURSE PLATFORM MULTI-ARMED BANDIT API ENDPOINT HOW DO WE SOLVE THEM? Single multi-armed bandit Limiting pool of items Batch update
  6. 6. THE FUTURE COMES HIGHLY RECOMMENDED
  7. 7. RECSYS FRANCE #RecSysFR @Bibblio_org @RVanDerPluijm

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