aritter on Nostr: While ranking training seems like to work now (except timing info :( ), for 1000 ...
While ranking training seems like to work now (except timing info :( ), for 1000 retrieved events I have 5 likes (rbr.io is retrieving lots more events than other clients because it shows all parent comments for now).
I guess the solution will be to get liked events as well from the user's relays to get liked / non-liked training data ratio closer to 50% then reweight the liked events to have very small, but much more learnable weights. For example if 5 like events are exchanged to 100, the liked data should have 0.05 item weighting when training.
Of course a modified Hessian for log likelihood needs to be computed for Newton-Ralph method for finding max log likelihood, so if somebody is good at math, they are welcome to help adding item weights to the computation :)
Published at
2023-04-19 05:07:34Event JSON
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"tags": [],
"content": "While ranking training seems like to work now (except timing info :( ), for 1000 retrieved events I have 5 likes (rbr.io is retrieving lots more events than other clients because it shows all parent comments for now).\n\nI guess the solution will be to get liked events as well from the user's relays to get liked / non-liked training data ratio closer to 50% then reweight the liked events to have very small, but much more learnable weights. For example if 5 like events are exchanged to 100, the liked data should have 0.05 item weighting when training.\n\nOf course a modified Hessian for log likelihood needs to be computed for Newton-Ralph method for finding max log likelihood, so if somebody is good at math, they are welcome to help adding item weights to the computation :)\n\n",
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}