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Testing low rank matrix factorization performance
require(recommenderlab) # Install this if you don't have it already
require(devtools) # Install this if you don't have this already
# Get additional recommendation algorithms
install_github("sanealytics", "recommenderlabrats")
data(MovieLense) # Get data
# Divvy it up
scheme <- evaluationScheme(MovieLense, method = "split", train = .9,
k = 1, given = 10, goodRating = 4)
scheme
# register recommender
recommenderRegistry$set_entry(
method="RSVD", dataType = "realRatingMatrix", fun=REAL_RSVD,
description="Recommender based on Low Rank Matrix Factorization (real data).")
# Some algorithms to test against
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)),
"Matrix Factorization" = list(name="RSVD", param=list(categories = 10,
lambda = 10,
maxit = 100))
)
# 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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