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Recommenderlab walkthrough 1-6
recommenderRegistry$get_entries(dataType = "realRatingMatrix")
# We have a few options
# Let's check some algorithms against each other
scheme <- evaluationScheme(MovieLense, method = "split", train = .9,
k = 1, given = 10, goodRating = 4)
scheme
algorithms <- list(
"random items" = list(name="RANDOM", param=list(normalize = "Z-score")),
"popular items" = list(name="POPULAR", param=list(normalize = "Z-score")),
"user-based CF" = list(name="UBCF", param=list(normalize = "Z-score",
method="Cosine",
nn=50, minRating=3)),
"item-based CF" = list(name="IBCF2", param=list(normalize = "Z-score"
))
)
# run algorithms, predict next n movies
results <- evaluate(scheme, algorithms, n=c(1, 3, 5, 10, 15, 20))
# Draw ROC curve
plot(results, annotate = 1:4, legend="topleft")
# See precision / recall
plot(results, "prec/rec", annotate=3)
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