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R: Unleash Machine Learning Techniques by Cory Lesmeister, Brett Lantz, Dipanjan Sarkar, Raghav Bali

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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 ...

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