### recommender for real-valued ratings
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")
p
calcPredictionAccuracy(p, getData(e, "unknown"), given = 15, goodRating = 5)
## evaluate a binary recommender
data(MSWeb)
MSWeb10 <- sample(MSWeb[rowCounts(MSWeb) >10,], 50)
e <- evaluationScheme(MSWeb10, method="split", train = 0.9,
k = 1, given = 3)
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="topNList", n = 10)
p
calcPredictionAccuracy(p, getData(e, "unknown"), given = 3)
calcPredictionAccuracy(p, getData(e, "unknown"), given = 3, byUser = TRUE)
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