Comments (4)
i believe predict_rank
predicts the rank of every item for each user in interactions
from lightfm.
This method is to be used when you're only evaluating a couple of interactions for every user, as is most common in evaluating models. It is quadratic in the number of nonzero interactions per user, so if you want to evaluate more items than that I suggest you use predict
instead.
I updated the docstring to reflect this in #111
from lightfm.
This method is to be used when you're only evaluating a couple of interactions for every user, as is most common in evaluating models. It is quadratic in the number of nonzero interactions per user, so if you want to evaluate more items than that I suggest you use
predict
instead.I updated the docstring to reflect this in #111
In that case shouldn't the precision_at_k code be made to run on predict instead of predict_rank/predict_ranks? I am asking because precision_at_k is also very slow.
from lightfm.
This method is to be used when you're only evaluating a couple of interactions for every user, as is most common in evaluating models. It is quadratic in the number of nonzero interactions per user, so if you want to evaluate more items than that I suggest you use
predict
instead.
I updated the docstring to reflect this in #111In that case shouldn't the precision_at_k code be made to run on predict instead of predict_rank/predict_ranks? I am asking because precision_at_k is also very slow.
@adsk2050 I agree. I tried to replicate prec@k using predict, but using predict results in a lot smaller prec@k scores than predict_rank. See #568.
from lightfm.
Related Issues (20)
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