The recommenderlab package of R offers the item-based collaborative filtering (ITCF) option to build a recommendation system. This is a very straightforward approach that just needs us to call the function and supply it with the necessary parameters. The parameters, in general, will have a lot of influence on the performance of the model; therefore, testing each parameter combination is the key to obtaining the best model for recommendations. The following are the parameters that can be passed to the Recommender function:
- Data normalization: Normalizing the ratings matrix is a key step in preparing the data for the recommendation engine. The process of normalization processes the ratings in the matrix by removing the rating bias. The possible values for this parameter are NULL, Center, and Z...
