library(magrittr)
library(recommenderlab)
### real valued recommender
data(Jester5k)
## create 90/10 split (known/unknown) for the first 500 users in Jester5k
e <- evaluationScheme(Jester5k[1:500,], method="split", train=0.9,
                      k=1, given=15)
e
## create a user-based CF recommender using training data
r <- Recommender(getData(e, "train"), "UBCF")
## create predictions for the test data using known ratings (see given above)
p <- predict(r, getData(e, "known"), type="ratings")
p
## compute error metrics averaged per user and then averaged over all
## recommendations
calcPredictionAccuracy(p, getData(e, "unknown"))
head(calcPredictionAccuracy(p, getData(e, "unknown"), byUser=TRUE))
## evaluate topNLists instead (you need to specify given and goodRating!)
p <- predict(r, getData(e, "known"), type="topNList", n=100)
p

calcPredictionAccuracy(p, getData(e, "unknown"), given=100, goodRating=5, byUser=T) %>% head

as(p,'list') %>% `[`(1)
#as(getData(e, "unknown"),'matrix')[1:5,1:50]
as(p,'matrix')[1,] %>% sort(decreasing = T)