Key Hyperparameter Type Description
lr Lr float64 learning rate
reg Reg float64 regularization strength
n_epochs NEpochs int number of epochs
n_factors NFactors int number of factors
random_state RandomState int random state (seed)
use_bias UseBias bool using bias
init_mean InitMean float64 mean of gaussian initial parameters
init_std InitStdDev float64 standard deviation of gaussian initial parameters
init_low InitLow float64 lower bound of uniform initial parameters
init_high InitHigh float64 upper bound of uniform initial parameters
n_user_clusters NUserClusters int number of user clusters
n_item_clusters NItemClusters int number of item clusters
type Type string type for KNN
user_based UserBased bool user based if true. otherwise item based
similarity Similarity string similarity metrics
k K int number of neighbors
min_k MinK int least number of neighbors
optimizer Optimizer string optimizer for optimization (sgd/bpr)
shrinkage Shrinkage int shrinkage strength of similarity
alpha Alpha float64 alpha value, depend on context

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