testing Rcgmin

Rcgmin test

data(MovieLense)

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).")

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 (L-BFGS-B)" = list(name="RSVD", param=list(categories = 50, 
                                                                   lambda = 10,
                                                                   maxit = 100)),
  "Matrix Factorization (Rcgmin)" = list(name="RSVD", param=list(categories = 50, 
                                                                 lambda = 10,
                                                                 optimize = Rcgmin,
                                                                 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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