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")

$ALS_realRatingMatrix
    Recommender method: ALS for realRatingMatrix
    Description: Recommender for explicit ratings based on latent 
      factors, calculated by alternating least squares algorithm.
    Reference: Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, Rong 
      Pan (2008).
    Large-Scale Parallel Collaborative Filtering for the Netflix Prize, 
      4th Int'l   
    Conf. Algorithmic Aspects in Information...