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How we built a "local popularity" recommender

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recsysfr

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

Science
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RECSYS FRANCE #RecSysFR @Bibblio_org @RVanDerPluijm
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
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
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
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
THE FUTURE COMES HIGHLY RECOMMENDED
RECSYS FRANCE #RecSysFR @Bibblio_org @RVanDerPluijm

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How we built a "local popularity" recommender