Collaborative Filtering: This approach builds a model from past behaviors, comparing items or users trough ratings, and in this case an User-Item Based Regression technique is used to predict the missing values. The Grey Wolf Optmizer (GWO) is used to find minimum loss value. The function returns: the prediction of the missing data and the gwo solution.
-
Xdata = Dataset Attributes. A matrix with users ratings about a set of items.
-
user_in_columns = Boolean that indicates if the user is in the column (user_in_column = True) or in the row (user_in_column = False). The default value is True.
-
pack_size = To find the weights, a metaheuristic know as Grey Wolf Optmizer (GWO) is used. The initial population (pack_size) helps to find the optimal solution. The default value is 25.
-
iterations = The total number of iterations. The defaul value is 100
Try pyRecommenderSystem: A Recommender System Library