import alpenglow.Getter as rs
import alpenglow as prs
[docs]class FactorExperiment(prs.OnlineExperiment):
"""FactorExperiment(dimension=10,begin_min=-0.01,begin_max=0.01,learning_rate=0.05,regularization_rate=0.0,negative_rate=0.0)
This class implements the well-known matrix factorization recommendation model [Koren2009]_
and trains it via stochastic gradient descent. The model is able to train on implicit data
as well using negative sample generation, see [X.He2016]_ and the **negative_rate** parameter.
.. [Koren2009] Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009).
.. [X.He2016] X. He, H. Zhang, M.-Y. Kan, and T.-S. Chua. Fast matrix factorization for online recommendation with implicit feedback. In SIGIR, pages 549–558, 2016.
Parameters
----------
dimension : int
The latent factor dimension of the factormodel.
begin_min : double
The factors are initialized randomly, sampling each element uniformly from the interval (begin_min, begin_max).
begin_max : double
See begin_min.
learning_rate : double
The learning rate used in the stochastic gradient descent updates.
regularization_rate : double
The coefficient for the L2 regularization term.
negative_rate : int
The number of negative samples generated after each update. Useful for implicit recommendation.
"""
def _config(self, top_k, seed):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05,
regularization_rate=0.0
))
updater.set_model(model)
learner = rs.ImplicitGradientLearner()
learner.add_gradient_updater(updater)
learner.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(**self.parameter_defaults(
negative_rate=0.0,
initialize_all=False,
seed=67439852,
filter_repeats=False,
))
learner.set_negative_sample_generator(negative_sample_generator)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner.set_gradient_computer(gradient_computer)
return (model, learner, [], [])