import alpenglow.Getter as rs
import alpenglow as prs
[docs]class AsymmetricFactorExperiment(prs.OnlineExperiment):
"""AsymmetricFactorExperiment(dimension=10,begin_min=-0.01,begin_max=0.01,learning_rate=0.05,regularization_rate=0.0,negative_rate=20,cumulative_item_updates=True,norm_type="exponential",gamma=0.8)
Implements the recommendation model introduced in [Koren2008]_.
.. [Paterek2007] Arkadiusz Paterek. „Improving regularized singular value decomposition for collaborative filtering”. In: Proc. KDD Cup Workshop at SIGKDD’07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining. San Jose, CA, USA, 2007, pp. 39–42.
.. [Koren2008] Koren, Yehuda. "Factorization meets the neighborhood: a multifaceted collaborative filtering model." Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008.
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.
norm_type : str
Type of time decay; either "constant", "exponential" or "disabled".
gamma : double
Coefficient of time decay in the case of **norm_type** == "exponential".
"""
def _config(self, top_k, seed):
model = rs.AsymmetricFactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
use_sigmoid=False,
norm_type="exponential",
gamma=0.8,
initialize_all=False
))
gradient_updater = rs.AsymmetricFactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.AsymmetricFactorModelUpdater()
simple_updater.set_model(model)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
gradient_computer.add_gradient_updater(gradient_updater)
negative_sample_generator = rs.UniformNegativeSampleGenerator(**self.parameter_defaults(
negative_rate=20,
initialize_all=False,
seed=928357823,
))
negative_sample_generator.add_updater(gradient_computer)
return (model, [negative_sample_generator, simple_updater], [])