Modeling, evaluation, and recommendations
In order to build and test our recommendation engines, we can use the same function, Recommender(), merely changing the specification for each technique. In order to see what the package can do and explore the parameters available for all six techniques, you can examine the registry. Looking at the following IBCF, we can see that the default is to find 30 neighbors using the cosine method with the centered data while the missing data is not coded as a zero:
> recommenderRegistry$get_entries(dataType = "realRatingMatrix") $IBCF_realRatingMatrix Recommender method: IBCF Description: Recommender based on item-based collaborative filtering (real data). Parameters: k method normalize normalize_sim_matrix alpha na_as_zero minRating 1 30 Cosine center FALSE 0.5 FALSE NA $PCA_realRatingMatrix Recommender method: PCA Description: Recommender based on PCA approximation (real data). Parameters: categories method normalize normalize_sim_matrix...
