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Another round of search tuning based on the results from image-recommendation-test
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We have an extra ~2.5k rated search results from image-recommendation-test, so we should probably re-tune our search results with these added to our training dataset.

erm ... I (@Cparle) probably will have to do this, seeing as I did the original search tuning and can re-do it without too much effort, but in the process I ought to make the process easier for anyone to do

Acceptance criteria:
[] a new set of parameters for search signals to be used in the search profile

  • an easier to follow process to generate those parameters

Event Timeline

So the easier-to-follow process for doing search tuning is


Tuning the results using the new data actually gives us a slightly worse precision@25 over all the labeled data than we have with the current tuned search (0.82 instead of 0.83) so I propose we keep what we have already