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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