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Mastering Machine Learning with R - Second Edition by Cory Lesmeister

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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")        $ALS_realRatingMatrix    Recommender method: ALS for realRatingMatrix    Description: Recommender for explicit ratings based on latent  factors, calculated by alternating least squares ...

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