alpenglow.offline.models package¶
Submodules¶
alpenglow.offline.models.AsymmetricFactorModel module¶
-
class
alpenglow.offline.models.AsymmetricFactorModel.
AsymmetricFactorModel
(**parameters)[source]¶ Bases:
alpenglow.offline.OfflineModel.OfflineModel
AsymmetricFactorExperiment(dimension=10,begin_min=-0.01,begin_max=0.01,learning_rate=0.05,regularization_rate=0.0,negative_rate=0,number_of_iterations=9)
Implements the recommendation model introduced in [Paterek2007].
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.
- number_of_iterations (int) – Number of times to iterate over the training data.
alpenglow.offline.models.FactorModel module¶
-
class
alpenglow.offline.models.FactorModel.
FactorModel
(**parameters)[source]¶ Bases:
alpenglow.offline.OfflineModel.OfflineModel
FactorExperiment(dimension=10,begin_min=-0.01,begin_max=0.01,learning_rate=0.05,regularization_rate=0.0,negative_rate=0.0,number_of_iterations=9)
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 using negative sample generation, see [X.He2016] and the negative_rate parameter.
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.
- number_of_iterations (int) – Number of times to iterate over the training data.
alpenglow.offline.models.NearestNeighborModel module¶
-
class
alpenglow.offline.models.NearestNeighborModel.
NearestNeighborModel
(num_of_neighbors=10)[source]¶ Bases:
alpenglow.offline.OfflineModel.OfflineModel
Item based nearest neighbor.
Parameters: num_of_neighbors (int) – Number of neighbors to consider.
alpenglow.offline.models.PopularityModel module¶
-
class
alpenglow.offline.models.PopularityModel.
PopularityModel
(**parameters)[source]¶ Bases:
alpenglow.offline.OfflineModel.OfflineModel
Recommends the most popular item from the set of items.