The CCO algorithm test for correlation with a statistic called the Log
Likelihood Ratio (LLR). This compares relative frequencies of 4 different
things 2 having to do with the entire dataset 2 having to do with the 2
events being compared for correlation. Popularity is normalized out of this
The only requirement is that someone performed the primary event on A and
the secondary event is correlated to that primary event.
the UR can recommend to a user who has only performed the secondary event
on B as long as that is in the model. Makes no difference what subset of
events the user has
Hi guys,
So I've been playing around with the UR algorithm and I would like to know 2
things if it is possible:
1- Does UR recommend items that are linked to primary event only? Like if item
A is pruchased (primary event) 1 time and item B is liked (secondary event) 50
times, does UR only
