Hello;
I am trying to get predictions after running the ALS model.
The model works fine. In the prediction/recommendation , I have about 30
,000 products and 90 Millions users.
When i try the predict all it fails.
I have been trying to formulate the problem as a Matrix multiplication where
I first get the product features, broadcast them and then do a dot product.
Its still very slow. Any reason why
here is a sample code
def doMultiply(x):
a = []
#multiply by
mylen = len(pf.value)
for i in range(mylen) :
myprod = numpy.dot(x,pf.value[i][1])
a.append(myprod)
return a
myModel = MatrixFactorizationModel.load(sc, "FlurryModelPath")
#I need to select which products to broadcast but lets try all
m1 = myModel.productFeatures().sample(False, 0.001)
pf = sc.broadcast(m1.collect())
uf = myModel.userFeatures()
f1 = uf.map(lambda x : (x[0], doMultiply(x[1])))
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