calcPredictionAccuracy: Calculate the Prediction Error for a Recommendation

In audachang/recommenderlab.test: Lab for Developing and Testing Recommender Algorithms

Description

Calculate prediction accuracy. For predicted ratings MAE (mean average error), MSE (means squared error) and RMSE (root means squared error) are calculated. For topNLists various binary classification metrics are returned.

Usage

calcPredictionAccuracy(x, data, ...)
## S4 method for signature 'realRatingMatrix,realRatingMatrix'
calcPredictionAccuracy(x, data, byUser=FALSE,...)
## S4 method for signature 'topNList,realRatingMatrix'
calcPredictionAccuracy(x, data, byUser=FALSE, given=NULL, goodRating=NA,...)
## S4 method for signature 'topNList,binaryRatingMatrix'
calcPredictionAccuracy(x, data, byUser=FALSE, given=NULL,...)

Arguments

x	Predicted items in a "topNList" or predicted ratings as a "realRatingMatrix"
data	Actual ratings by the user as a "RatingMatrix"
byUser	logical; Should the errors be averaged by user or over all recommendations?
given	how many items were given to create the predictions.
goodRating	threshold for determining what rating is a good rating. Used only if x is a topNList and data is a "realRatingMatrix".
...	further arguments.

Value

Returns a vector with the average measures for byUser=TRUE. Otherwise, a matrix with a row for each user is returned.

References

Asela Gunawardana and Guy Shani (2009). A Survey of Accuracy Evaluation Metrics of Recommendation Tasks, Journal of Machine Learning Research 10, 2935-2962.

See Also

topNList, binaryRatingMatrix, realRatingMatrix.

Examples

### real valued recommender  
data(Jester5k)

## create 90/10 split (known/unknown) for the first 500 users in Jester5k
e <- evaluationScheme(Jester5k[1:500,], method="split", train=0.9, 
    k=1, given=15)
e

## create a user-based CF recommender using training data
r <- Recommender(getData(e, "train"), "UBCF")

## create predictions for the test data using known ratings (see given above)
p <- predict(r, getData(e, "known"), type="ratings")
p

## compute error metrics averaged per user and then averaged over all 
## recommendations
calcPredictionAccuracy(p, getData(e, "unknown"))
head(calcPredictionAccuracy(p, getData(e, "unknown"), byUser=TRUE))

## evaluate topNLists instead (you need to specify given and goodRating!)
p <- predict(r, getData(e, "known"), type="topNList")
p
calcPredictionAccuracy(p, getData(e, "unknown"), given=15, goodRating=5) 
  
## evaluate a binary recommender
data(MSWeb)
MSWeb10 <- sample(MSWeb[rowCounts(MSWeb) >10,], 50)

e <- evaluationScheme(MSWeb10, method="split", train=0.9, 
    k=1, given=3)
e
  
## create a user-based CF recommender using training data
r <- Recommender(getData(e, "train"), "UBCF")
  
## create predictions for the test data using known ratings (see given above)
p <- predict(r, getData(e, "known"), type="topNList", n=10)
p

calcPredictionAccuracy(p, getData(e, "unknown"), given=3)

audachang/recommenderlab.test documentation built on May 20, 2019, 1:27 p.m.