将先验rulset部署到R中的数据集 [英] deploying apriori rulsets to the dataset in R

问题描述

我对R中先验规则的部署有疑问.我基本上想为每个客户分配一个predcition(item)和一个置信度值，这样我就可以创建一个简单的推荐系统，因此下面是我的规则集的一个子集，其中我已经获得，

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%)

以下是我仅希望有1个客户实现的目标的简单表示；这是在购物篮或真值表数据中.

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 |面包|酒|奶酪Pred1 Conf1 Pred2 Conf2

ID|Bread|Wine| Cheese Pred1 Conf1 Pred2 Conf2

1 | 1 | 1 | 1肉| 0.54 |水果| 0.43

1 | 1 | 1 | 1 meat| 0.54| fruit| 0.43

这可以通过简单地将数据集连接到IBM SPSS Modeler中的模型块来完成，但是在R中似乎并不容易.

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.

有人可以为此提供R代码的解决方案，还是提供一个简单的指南呢?

Can anyone provide me with a solution in R code on this or a simple guide in doing this?

推荐答案

Package Recommendationerlab可以满足您的要求(减去显示的可信度).这是一些代码(改编自recommenerlab的文档)，该代码从Groceries数据集中学习了一个推荐器模型，并将其应用于前10个交易:

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"

以下是创建推荐器时可以使用的参数.

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

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