def _fit(self, recommender_data, users, items, matrix):
model = rs.FactorModel(**self.parameter_defaults(
begin_min=-0.01,
begin_max=0.01,
dimension=10,
initialize_all=False,
seed=254938879,
))
updater = rs.FactorModelGradientUpdater(**self.parameter_defaults(
learning_rate=0.05, regularization_rate=0.0))
updater.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(negative_rate=0))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner = rs.OfflineIteratingImplicitLearner(**self.parameter_defaults(
seed=254938879,
number_of_iterations=9,
))
learner.set_gradient_computer(gradient_computer)
learner.set_negative_sample_generator(negative_sample_generator)
learner.set_model(model)
learner.set_recommender_data(recommender_data)
learner.add_gradient_updater(updater)
return (model, learner)
def _fit(self, recommender_data, users, items, matrix):
model = rs.SvdppModel(**self.parameter_defaults(
begin_min=self.parameter_default("begin_min", -0.01),
begin_max=self.parameter_default("begin_max", 0.01),
dimension=self.parameter_default("dimension", 10),
use_sigmoid=False,
norm_type="disabled",
gamma=1,
user_vector_weight=0.5,
history_weight=0.5))
gradient_updater = rs.SvdppModelGradientUpdater(
**self.parameter_defaults(
learning_rate=0.05,
cumulative_item_updates=False,
))
gradient_updater.set_model(model)
simple_updater = rs.SvdppModelUpdater()
simple_updater.set_model(model)
negative_sample_generator = rs.UniformNegativeSampleGenerator(
**self.parameter_defaults(negative_rate=9))
negative_sample_generator.set_train_matrix(matrix)
negative_sample_generator.set_items(items)
point_wise = rs.ObjectiveMSE()
gradient_computer = rs.GradientComputerPointWise()
gradient_computer.set_objective(point_wise)
gradient_computer.set_model(model)
learner = rs.OfflineIteratingImplicitLearner(**self.parameter_defaults(
seed=254938879,
number_of_iterations=20,
))
learner.set_gradient_computer(gradient_computer)
learner.set_negative_sample_generator(negative_sample_generator)
learner.set_model(model)
learner.set_recommender_data(recommender_data)
learner.add_gradient_updater(gradient_updater)
learner.add_early_simple_updater(simple_updater)
return (model, learner)