Collaborative Filtering
crs(dataset, id, prod, pred, rate, data_filter = "", envir = parent.frame())
| dataset | Dataset |
|---|---|
| id | String with name of the variable containing user ids |
| prod | String with name of the variable with product ids |
| pred | Products to predict for |
| rate | String with name of the variable with product ratings |
| data_filter | Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "training == 1") |
| envir | Environment to extract data from |
A data.frame with the original data and a new column with predicted ratings
See https://radiant-rstats.github.io/docs/model/crs.html for an example in Radiant
summary.crs to summarize results
plot.crs to plot results if the actual ratings are available
crs(ratings, id = "Users", prod = "Movies", pred = c("M6", "M7", "M8", "M9", "M10"), rate = "Ratings", data_filter = "training == 1") %>% str()#> List of 16 #> $ recommendations:'data.frame': 5 obs. of 8 variables: #> ..$ Users : Factor w/ 11 levels "U1","U2","U3",..: 11 11 11 11 11 #> ..$ product : Factor w/ 5 levels "M6","M7","M8",..: 1 2 3 4 5 #> ..$ rating : int [1:5] NA NA NA NA NA #> ..$ average : num [1:5] 3.3 2.7 3.5 2.9 4.1 #> ..$ cf : num [1:5] 4.1 2.08 1.7 2.13 2.71 #> ..$ ranking : int [1:5] NA NA NA NA NA #> ..$ avg_rank: int [1:5] 3 5 2 4 1 #> ..$ cf_rank : int [1:5] 1 4 5 3 2 #> $ rcf :'data.frame': 1 obs. of 6 variables: #> ..$ Users: Factor w/ 11 levels "U1","U2","U3",..: 11 #> ..$ M6 : int 1 #> ..$ M7 : int 4 #> ..$ M8 : int 5 #> ..$ M9 : int 3 #> ..$ M10 : int 2 #> $ cf :'data.frame': 1 obs. of 6 variables: #> ..$ Users: Factor w/ 11 levels "U1","U2","U3",..: 11 #> ..$ M6 : num 4.1 #> ..$ M7 : num 2.08 #> ..$ M8 : num 1.7 #> ..$ M9 : num 2.13 #> ..$ M10 : num 2.71 #> $ rank : int [1, 1:5] 1 4 5 3 2 #> ..- attr(*, "dimnames")=List of 2 #> .. ..$ : chr "11" #> .. ..$ : NULL #> $ ract :'data.frame': 1 obs. of 6 variables: #> ..$ Users: Factor w/ 11 levels "U1","U2","U3",..: 11 #> ..$ M6 : int NA #> ..$ M7 : int NA #> ..$ M8 : int NA #> ..$ M9 : int NA #> ..$ M10 : int NA #> $ act :'data.frame': 1 obs. of 6 variables: #> ..$ Users: Factor w/ 11 levels "U1","U2","U3",..: 11 #> ..$ M6 : int NA #> ..$ M7 : int NA #> ..$ M8 : int NA #> ..$ M9 : int NA #> ..$ M10 : int NA #> $ ravg :'data.frame': 1 obs. of 5 variables: #> ..$ M6 : int 3 #> ..$ M7 : int 5 #> ..$ M8 : int 2 #> ..$ M9 : int 4 #> ..$ M10: int 1 #> $ avg :'data.frame': 1 obs. of 5 variables: #> ..$ M6 : num 3.3 #> ..$ M7 : num 2.7 #> ..$ M8 : num 3.5 #> ..$ M9 : num 2.9 #> ..$ M10: num 4.1 #> $ evar : chr [1:9] "M2" "M3" "M4" "M5" ... #> $ df_name : chr "ratings" #> $ vars : chr [1:9] "M2" "M3" "M4" "M5" ... #> $ id : chr "Users" #> $ prod : chr "Movies" #> $ pred : chr [1:5] "M6" "M7" "M8" "M9" ... #> $ rate : chr "Ratings" #> $ data_filter : chr "training == 1" #> - attr(*, "class")= chr [1:2] "crs" "list"