Comments (8)
If you update the user_items
matrix, you can now recalculate a user factor and get updated recommendations by running model.recommend(userid, user_items, recalculate_user=True)
.
You can also get recommendations for new users by passing a column vector as user_items
and userid=0
.
It's still not incremental training because item factors are not recalculated, but maybe it's helpful.
from implicit.
Setting recalculate_user=True
works, but seems to be quite slow, since it does a complete matrix inversion. In my tests, over 99% of the time would be spent on this operation.
One could speed this up by the conjugate gradient method, detailed here: https://www.benfrederickson.com/fast-implicit-matrix-factorization/.
The relevant part of that page would give something like this.
def factor_user_cg(Cui, X, Y, regularization, cg_steps=3):
users, factors = X.shape
# we could cache this
YtY = Y.T.dot(Y) + regularization * np.eye(factors)
# random start
x = np.random.rand(factors) * 0.01
# calculate residual r = (YtCuPu - (YtCuY.dot(Xu), without computing YtCuY
r = -YtY.dot(x)
for i, confidence in nonzeros(Cui, u):
r += (confidence - (confidence - 1) * Y[i].dot(x)) * Y[i]
p = r.copy()
rsold = r.dot(r)
for _ in range(cg_steps):
# calculate Ap = YtCuYp - without actually calculating YtCuY
Ap = YtY.dot(p)
for i, confidence in nonzeros(Cui, u):
Ap += (confidence - 1) * Y[i].dot(p) * Y[i]
# standard CG update
alpha = rsold / p.dot(Ap)
x += alpha * p
r -= alpha * Ap
rsnew = r.dot(r)
p = r + (rsnew / rsold) * p
rsold = rsnew
return x
from implicit.
Like @leodesigner was saying - this isn't supported in this library right now, but you can build this on top of implicit with some effort.
Adding support for incremental training would be a good feature for this library.
from implicit.
You have to maintain your own item/user matrix and manage new/expired users or items.
Then run matrix factorization periodically.
from implicit.
@leodesigner
thanks for your answer
matrix factorization once again waste time
your mean that can't reuse the last time result of matrix factorization
from implicit.
You can actually reuse last results as an initialization - this is better than starting from random initialization. In this case only new item/users should be initialized randomly or as an average of similar items/users.
In my case my input matrix is about 10000x10000 and one iteration of the MF algorithm takes about 0.0125s on my server. (I am reusing last results).
from implicit.
@leodesigner
are you use implicit
to do real-time recommend? or it's can do it
from implicit.
I am using implicit to do calculations once a 10-60 minutes. However the results are used in realtime web app (client browser side item sorting based on cousine distance).
from implicit.
Related Issues (20)
- numpy has teriminated the `get_info` method HOT 4
- Evaluation module not working HOT 1
- Adding `numpy` to `requirements.txt`
- pip install implicit is not work to use GPU HOT 3
- Incremental update with old users but new items HOT 4
- als explain method bug HOT 1
- Usage of the trained model on testing.
- ranking metrics fail when model trained on CPU but not GPU?; implicit 0.7.2 HOT 1
- Python issue HOT 4
- Is it possible to compare scores across multiple results?
- function get_lastfm() error
- getting index error while using recommend function while ALS algorithm
- What is a good enough Precision@K and MAP@K value?
- BPR results not reproducible using multiple threads
- gpu issue on colab (implicit==0.7.2 / cuda ==12.1) HOT 1
- Potential error in the calculation of precision@k HOT 2
- Early stopping for ALS
- How recommend method works in Nearest Neighbour Models? HOT 3
- Potential bug in calculation of gradient updates for BPR
- IMPLICIT: No CUDA extension has been built, can't train on GPU HOT 1
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from implicit.