The function returns the path of a folder in result_experiments The function guarantees that the folder is not already existent and it create

Shapiro-Wilk Test of Normality if less than 50 participants; the Kolmogorov-Smirnov Test if equal or more than 50 Use the Shapiro-Wilk test f

Read .rating file and Return dok matrix. The first line of .rating file is: num_users\t num_items

0. user_id 1. original_id 2. age ---catgorical 3. region ---catgorical

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

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

Config is dumped as a json file

:param URM_train_new: :param estimate_item_similarity_for_cold_users: Set to TRUE if you want to estimate the item-item similarity f

This function creates a Train, Test, Validation split with negative items sampled The split is perfomed user-wise, hold 1 out for validation

Increment a vector element by a double.

r""" Pairwise Loss from Bayesian Personalized Ranking. \log \sigma(pos - neg) where \sigma is the sigmoid function, we try to set the ra

Check whether the keys of the dictionary are string. If not, transforms them into strings :param dict_to_save: :return:

The function computes the gini diversity of the given recommended item distribution. This is NOT the Gini index, rather a variation of it suc

Compute the items scores using the native function for the MF algorithm :param posargs: :param kwargs: :return:

:param user_id_array: array containing the user indices whose recommendations need to be computed :param items_to_compute:

Compute item scores with the ItemKNN model :param user_id_array: :param item_scores: :return:

Construct the model; main part of it goes here

Construct the model; main part of it goes here

Create placeholders for our model

Constructs the user/item memories and user/item external memory/outputs Also add the embedding lookups

Constructs the user/item memories and user/item external memory/outputs Also add the embedding lookups

Converts a list containing seconds into an equivalent list with a bigger time unit adjusting standard deviation as well :param data_list: