Class

A matrix containing ratings (typically 1-5 stars, etc.).

Objects from the Class

Objects can be created by calls of the form new("realRatingMatrix", data = m), where m is sparse matrix of class dgCMatrix in package Matrix or by coercion from a regular matrix, a data.frame containing user/item/rating triplets as rows, or a sparse matrix in triplet form (dgTMatrix in package Matrix).

Slots

data:: Object of class "dgCMatrix", a sparse matrix defined in package Matrix. Note that this matrix drops NAs instead of zeroes. Operations on "dgCMatrix" potentially will delete zeroes.
normalize:: NULL or a list with normalization factors.

Extends

Class "ratingMatrix", directly.

Methods

coerce: signature(from = "matrix", to = "realRatingMatrix"): Note that unknown ratings have to be encoded in the matrix as NA and not as 0 (which would mean an actual rating of 0).
coerce: signature(from = "realRatingMatrix", to = "matrix")
coerce: signature(from = "data.frame", to = "realRatingMatrix"): coercion from a data.frame with three columns. Col 1 contains user ids, col 2 contains item ids and col 3 contains ratings.
coerce: signature(from = "realRatingMatrix", to = "data.frame"): produces user/item/rating triplets.
coerce: signature(from = "realRatingMatrix", to = "dgTMatrix")
coerce: signature(from = "dgTMatrix", to = "realRatingMatrix")
coerce: signature(from = "realRatingMatrix", to = "dgCMatrix")
coerce: signature(from = "dgCMatrix", to = "realRatingMatrix")
coerce: signature(from = "realRatingMatrix", to = "ngCMatrix")
binarize: signature(x = "realRatingMatrix"): create a "binaryRatingMatrix" by setting all ratings larger or equal to the argument minRating as 1 and all others to 0.
getTopNLists: signature(x = "realRatingMatrix"): create top-N lists from the ratings in x. Arguments are n (defaults to 10), randomize (default is NULL) and minRating (default is NA). Items with a rating below minRating will not be part of the top-N list. randomize can be used to get diversity in the predictions by randomly selecting items with a bias to higher rated items. The bias is introduced by choosing the items with a probability proportional to the rating \((r-min(r)+1)^{randomize}\). The larger the value the more likely it is to get very highly rated items and a negative value for randomize will select low-rated items.
removeKnownRatings: signature(x = "realRatingMatrix"): removes all ratings in x for which ratings are available in the realRatingMatrix (of same dimensions as x) passed as the argument known.
rowSds: signature(x = "realRatingMatrix"): calculate the standard deviation of ratings for rows (users).
colSds: signature(x = "realRatingMatrix"): calculate the standard deviation of ratings for columns (items).

Examples

## create a matrix with ratings
m <- matrix(sample(c(NA,0:5),100, replace=TRUE, prob=c(.7,rep(.3/6,6))),
  nrow=10, ncol=10, dimnames = list(
      user=paste('u', 1:10, sep=''),
      item=paste('i', 1:10, sep='')
    ))
m
#>      item
#> user  i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
#>   u1  NA NA  1  3  0 NA  1 NA  2  NA
#>   u2  NA NA  5 NA NA NA NA  5 NA  NA
#>   u3   4  1 NA NA  1  1 NA  1 NA  NA
#>   u4  NA NA NA NA  0  5 NA NA NA   2
#>   u5  NA NA NA NA NA NA NA  2 NA  NA
#>   u6   0  1 NA NA NA NA NA  4  5  NA
#>   u7   4 NA NA  1 NA NA  5  4 NA  NA
#>   u8  NA NA  0  4 NA  5 NA NA  4  NA
#>   u9   0 NA NA  1 NA NA  2 NA NA  NA
#>   u10 NA NA NA NA  3 NA NA NA NA  NA

## Coerce into a realRatingMatrix
r <- as(m, "realRatingMatrix")
r
#> 10 x 10 rating matrix of class ‘realRatingMatrix’ with 32 ratings.

## get some information
dimnames(r)
#> $user
#>  [1] "u1"  "u2"  "u3"  "u4"  "u5"  "u6"  "u7"  "u8"  "u9"  "u10"
#> 
#> $item
#>  [1] "i1"  "i2"  "i3"  "i4"  "i5"  "i6"  "i7"  "i8"  "i9"  "i10"
#> 
rowCounts(r) ## number of ratings per user
#>  u1  u2  u3  u4  u5  u6  u7  u8  u9 u10 
#>   5   2   5   3   1   4   4   4   3   1 
colCounts(r) ## number of ratings per item
#>  i1  i2  i3  i4  i5  i6  i7  i8  i9 i10 
#>   4   2   3   4   4   3   3   5   3   1 
colMeans(r) ## average item rating
#>       i1       i2       i3       i4       i5       i6       i7       i8 
#> 2.000000 1.000000 2.000000 2.250000 1.000000 3.666667 2.666667 3.200000 
#>       i9      i10 
#> 3.666667 2.000000 
nratings(r) ## total number of ratings
#> [1] 32
hasRating(r) ## user-item combinations with ratings
#> 10 x 10 sparse Matrix of class "ngCMatrix"
#>   [[ suppressing 10 column names ‘i1’, ‘i2’, ‘i3’ ... ]]
#>      item
#> user                     
#>   u1  . . | | | . | . | .
#>   u2  . . | . . . . | . .
#>   u3  | | . . | | . | . .
#>   u4  . . . . | | . . . |
#>   u5  . . . . . . . | . .
#>   u6  | | . . . . . | | .
#>   u7  | . . | . . | | . .
#>   u8  . . | | . | . . | .
#>   u9  | . . | . . | . . .
#>   u10 . . . . | . . . . .

## histogram of ratings
hist(getRatings(r), breaks="FD")


## inspect a subset
image(r[1:5,1:5])


## coerce it back to see if it worked
as(r, "matrix")
#>      item
#> user  i1 i2 i3 i4 i5 i6 i7 i8 i9 i10
#>   u1  NA NA  1  3  0 NA  1 NA  2  NA
#>   u2  NA NA  5 NA NA NA NA  5 NA  NA
#>   u3   4  1 NA NA  1  1 NA  1 NA  NA
#>   u4  NA NA NA NA  0  5 NA NA NA   2
#>   u5  NA NA NA NA NA NA NA  2 NA  NA
#>   u6   0  1 NA NA NA NA NA  4  5  NA
#>   u7   4 NA NA  1 NA NA  5  4 NA  NA
#>   u8  NA NA  0  4 NA  5 NA NA  4  NA
#>   u9   0 NA NA  1 NA NA  2 NA NA  NA
#>   u10 NA NA NA NA  3 NA NA NA NA  NA

## coerce to data.frame (user/item/rating triplets)
as(r, "data.frame")
#>    user item        rating
#> 7    u1   i3  1.000000e+00
#> 10   u1   i4  3.000000e+00
#> 14   u1   i5 2.225074e-308
#> 21   u1   i7  1.000000e+00
#> 29   u1   i9  2.000000e+00
#> 8    u2   i3  5.000000e+00
#> 24   u2   i8  5.000000e+00
#> 1    u3   i1  4.000000e+00
#> 5    u3   i2  1.000000e+00
#> 15   u3   i5  1.000000e+00
#> 18   u3   i6  1.000000e+00
#> 25   u3   i8  1.000000e+00
#> 16   u4   i5 2.225074e-308
#> 19   u4   i6  5.000000e+00
#> 32   u4  i10  2.000000e+00
#> 26   u5   i8  2.000000e+00
#> 2    u6   i1 2.225074e-308
#> 6    u6   i2  1.000000e+00
#> 27   u6   i8  4.000000e+00
#> 30   u6   i9  5.000000e+00
#> 3    u7   i1  4.000000e+00
#> 11   u7   i4  1.000000e+00
#> 22   u7   i7  5.000000e+00
#> 28   u7   i8  4.000000e+00
#> 9    u8   i3 2.225074e-308
#> 12   u8   i4  4.000000e+00
#> 20   u8   i6  5.000000e+00
#> 31   u8   i9  4.000000e+00
#> 4    u9   i1 2.225074e-308
#> 13   u9   i4  1.000000e+00
#> 23   u9   i7  2.000000e+00
#> 17  u10   i5  3.000000e+00

## binarize into a binaryRatingMatrix with all 4+ rating a 1
b <- binarize(r, minRating=4)
b
#> 10 x 10 rating matrix of class ‘binaryRatingMatrix’ with 12 ratings.
as(b, "matrix")
#>        i1    i2    i3    i4    i5    i6    i7    i8    i9   i10
#> u1  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> u2  FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#> u3   TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> u4  FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#> u5  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> u6  FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#> u7   TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE
#> u8  FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE
#> u9  FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> u10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Class "realRatingMatrix": Real-valued Rating Matrix

See also

Objects from the Class

Slots

Extends

Methods

Examples