问题
I have a question regarding apriori rule deployment in R. I basically want to assign a predcition(item) and a confidence value to each customer so I can create a simple recommending system, so below is a subset of my rule set which I have obtained,
bread&wine -> meat (confidence 54%)
cheese -> fruit (confidence 43%)
bread&cheese -> frozveg (confidence 24%)
and the following is simple representation of what I want to achieve with just 1 customer; this is in a basket or truth-table data.
ID|Bread|Wine| Cheese Pred1 Conf1 Pred2 Conf2
1 | 1 | 1 | 1 meat| 0.54| fruit| 0.43
This can be done by simply connecting the dataset to the model nugget in IBM SPSS Modeler, but it does not seem easy in R.
Can anyone provide me with a solution in R code on this or a simple guide in doing this?
回答1:
Package recommenderlab does what you want (minus showing the confidence). Here is some code (adapted from the documentation of recommenerlab) which learns a recommender model from the Groceries data set and applies it to the first 10 transactions:
library(recommenderlab)
data(Groceries)
dat <- as(Groceries, "binaryRatingMatrix")
rec <- Recommender(dat, method = "AR",
parameter=list(support = 0.0005, conf = 0.5, maxlen = 5))
getModel(rec)
$description
[1] "AR: rule base"
$rule_base
set of 38365 rules
$support
[1] 5e-04
$confidence
[1] 0.5
$maxlen
[1] 5
$measure
[1] "confidence"
$verbose
[1] FALSE
$decreasing
[1] TRUE
pred <- predict(rec, dat[1:5,])
as(pred, "list")
[[1]]
[1] "whole milk" "rolls/buns" "tropical fruit"
[[2]]
[1] "whole milk"
[[3]]
character(0)
[[4]]
[1] "yogurt" "whole milk" "cream cheese " "soda"
[[5]]
[1] "whole milk"
Here are the parameters you can use when you create the recommender.
recommenderRegistry$get_entry("AR", dataType = "binaryRatingMatrix")
Recommender method: AR
Description: Recommender based on association rules.
Parameters:
support confidence maxlen measure verbose decreasing
1 0.1 0.3 2 confidence FALSE TRUE
来源:https://stackoverflow.com/questions/38394113/deploying-apriori-rulsets-to-the-dataset-in-r