The Public Service Algorithm: Personalization at the CBC | MEDIUM


‘CBC generates almost 500 new pieces of content each day so we need a system that can make real-time predictions and be updated with new pieces of content as they emerge.’

Canada’s public broadcaster is refining a new form of recommendation engine based on session preferences. Many recommender algorithms predict interest by either by

  • recalling a user’s prior searches (‘you liked that song so you my like this one’), or
  • inferring interest by similarities with searches by others (‘other people that liked this book also liked these’).

The per session approach predicts a trajectory of interest based on the items selected earlier in the visit.

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The Public Service Algorithm: Personalization at the CBC
MEDIUM | October 1, 2018 | by Jason Cornell

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