Comments (2)
Hi @jmadison222! Did you manage to solve this? Or ended up using another library? Thanks!
from python-recsys.
The library does not allow to do this.
But you can do something that resembles what SVD.recommend would return (and then, still recompute SVD overnight):
svd.add_tuple((1.0, 1, 88))
svd.add_tuple((1.0, 2, 88))
svd.add_tuple((1.0, 61, 88))
orig_recs = svd.recommend(88, n=3, is_row=False, only_unknowns=True)
NUM_SIMILARS = 5 # or 10 or 20
new_items = [1,2,61]
new_recs = []
for new_item_id in new_items:
new_recs += svd.similar(new_item_id, NUM_SIMILARS)
# TODO: combine orig_recs and the more fresh, relevant recommendations coming from the new_recs
from python-recsys.
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from python-recsys.