Regarding overfitting, don't forget dithering. That can be the most
important single step you take in building a good recommender.
Dithering can be inversely proportional to amount of exposures so far if
you like to give novel items more exposure.
This doesn't have to be very fancy. I have had very good results by
generating a long list of recommendations, computing a pseudo score based
on rank, adding a bit of noise and resorting. I also scanned down the list
and penalized items that showed insufficient diversity. Then I resorted
again. Typically, the pseudo score was something like exp(-r) where r is
The noise scale is adjusted to leave a good proportion of originally
recommended items in the first page. It could have easily been scaled by
1/sqrt(exposures) to let the newbies move around more.
The parameters here should be adjusted a bit based on experiments, but a
heuristic first hack works pretty well as a start.
On Sun, Nov 12, 2017 at 10:34 PM, Pat Ferrel <[EMAIL PROTECTED]> wrote: