:param epochs: :param batch_size: :param num_factors: Embedding size of MF model :param layers: MLP layers. Note tha

Items are assumed to be on rows :param dataMatrix: :param K1: :param B: :return:

:param mapper_dict: must be a mapper of [token] -> index :param indices_to_remove: :return:

The function eliminates the values associated to feature occurring in less than the minimal percentage of items or more then the max. Shape o

This function performs the hyperparameter optimization for a content-based recommender :param recommender_class: Class of the recommender

This function performs the hyperparameter optimization for a hybrid collaborative and content-based recommender :param recommender_class:

Returns the root of the folder tree which contains all of the dataset data/splits and files :return: Dataset_name/

Read a feature file and bind :param file: feature file

Read a feature file and bind :param file: :return:

Construct dataset :param X_user: user structured data :param X_item: item structured data :return:

* * check if file exisits */

The train set is defined as all data except the one of that fold, which is the test :return: URM_train, URM_validation, URM_test

Obtain for each user a positive item lists (associate uid -> [iid]) :param file: train file :return:

blasified

Parse the file (train or test) and load in memory for future usage. Return a list of Interaction

read raw data :param file: data file :return: structured data

:param recommender_input_args: :param parameter_search_space: :param metric_to_optimize: :param n_cases: :pa

Wrapper Function for loss with l1/l2 regularization :param loss_type: str, see rbase.utils.tfutils.Loss for Keys :param name