On 1/17/18 9:29 AM, leutrim.kal...@gmail.com wrote:
Hello everyone,
I am implementing a time-dependent Recommender System which applies BPR
(Bayesian Personalized Ranking), where Stochastic Gradient Ascent is used to
learn the parameters of the model. Such that, one iteration involves sampling
randomly the quadruple (i.e. userID, positive_item, negative_item, epoch_index)
for n times, where n is the total number of positive feedbacks (i.e. the number
of ratings given to all items). But, as my implementation takes too much time
for learning the parameters (since it requires 100 iterations to learn the
parameters), I was wondering if there is a way to improve my code and speed up
the learning process of the parameters.
Please find the code of my implementation (the update function named as
updateFactors is the one that learns the parameters, and such that I guess is
the one that should be improved in order to speed up the process of learning
the parameter values) in the following link:
https://codereview.stackexchange.com/questions/183707/speeding-up-the-implementation-of-stochastic-gradient-ascent-in-python
It looks like you have lists that you are doing "in" and "remove"
operations on. I don't know how long these lists are, but sets will be
much faster in general. You'll have to replace random.choice() with
random.choice(list(...)), since you can't random.choice from a set.
That might negate the benefits, but it's worth a try.
--Ned.
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