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The included baseline recommendation methods are the global mean rating (Global Average), item’s mean rating (Item Average), user's mean rating (User Average), as well as an the Most Popular Item Recommendation method that recommends to all users the items that are most popular.
To train a model for the rating prediction task we use the following commands:
data("ml100k")
d <- defineData(ml100k)
e <- evalModel(d, folds = 2)
globAv <- evalPred(e, alg = "globalaverage")
globAv
# Algorithm names might be matched on the registry partially.
itemAv <- evalPred(e, "itemAver")
itemAv
userAv <- evalPred(e, "useraverage")
userAv
Please notice that the baseline recommenders do not require any other argument.
The returned object are of type algAverageClass.
To get more details about the slots read the reference manual.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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