ludovikcoba / rrecsys Goto Github PK

In documentation of rrecsys, the configuration of alg mentions alg = "mostpopular".
However, in source code is just alg = "Popular".

Hi,

is there a possibility to run an out-of-sample prediction in rrecsys?
In the way I understood the package, only previously trained data can be used for prediction. For my purposes, I need to create a model on a training data set and predict values for a test data set.
In other packages this is easily possible but in rrecsys I haven't found a way to do so yet.

I am getting this error

Error in V[j, ] <- V[j, ] + lambda * (sigma * (-U[u, ]) - regJ * V[j, : NAs are not allowed in sub-scripted
assignments

on default settings, although there are no NAs in the dataset. When i set the lambda to 0.001 then I get the following error;

Error in U[u, ] <- U[u, ] + lambda * (sigma * (V[i, ] - V[j, ]) - regU * :
replacement has length zero

AprUsg1.zip

i'm trying to use the bpr algorithm following the example given in the vignette:

data("mlLatest100k")
ML <- defineData(mlLatest100k, minimum = .5, maximum = 5, intScale = TRUE)
subML <- ML[rowRatings(ML)>=40, colRatings(ML)>=30]
bpr <- rrecsys(subML, "BPR", k = 10, randomInit = FALSE, 
               regU = .0025, regI = .0025, regJ = 0.0025, updateJ = TRUE)

Unfortunately, even if i let this running for an hour, i don't reach a solution, it just keeps on running.

I tried adding a stopping criterion and / or setting randomInit to TRUE with no effect:

setStoppingCriteria(nrLoops = 10)

Am i doing something wrong?

I am using R package ‘rrecsys’ version 0.9.7.3.1 and yet I cannot use eval_nDCG.
The function is documented in the help reference (?) but I cannot use it.
I get the following error: could not find function "eval_nDCG"
Am I doing something wrong? This is unusual and my first experience of a function documented and yet not available.

Hey thank you for implementing this recommender system library. I have been using it to deal with some big dataset, e.g. 22531 users and 43968 items. To define the dataSet I just run out of memory since it requires a matrix. Would you be able to change it to a sparse matrix?

Another thing is with the returned metric values. To do statistical tests, e.g. t-tests, more samples are needed and by increasing number of folds just to get over 30 samples is simply too time costly. I have tried modifying the evalRec.R file with
# Combine the normal res with the nDCG per folds as a list
res <- list(res, nDCG_folds)
where nDCG_folds contains all the nDCG per users. But I feel like there could be a much more elegant solution where the user can easily access the metric value per user with result. I would be happy to try and help implement something.

Function recommendMF does not work (while recommendHPR does).
It looks like an error in line to call "UseMethod("recommendMF", model)".

userBasedRec <- rrecsys(ML, "ubknn", simFunct="Pearson")
recommendMF(userBasedRec,topN=3, pt=1)
Error in UseMethod("recommendMF", model) : 
  no applicable method for 'recommendMF' applied to an object of class "c('UBclass', 'SimilBasedClass')"

I'm trying to reproduce recommendHPR example:

myratings <- matrix(sample(c(0:5), size = 200, replace = TRUE,  prob = c(.6,.08,.08,.08,.08,.08)), nrow = 20, byrow = TRUE)
myratings <- defineData(myratings)
r <- rrecsys(myratings, alg = "FunkSVD", k = 2)
rec <- recommendHPR(r)

But myratings matrix has 0 as minimun value, so I've included minimum = 0 at defineData function. In this case, recommendHPR returns a list of NA's. Have I done something wrong?

If I have a training set x , and a test set y ,than I execute R command like this:
x = defineData(trainingset,sparseMatrix = TRUE)
y = defineData(testset,sparseMatrix =TRUE)
r <- rrecsys(test, alg = "UBKNN", simFunct = "cos") //use UBKNN algorithm

then how can I compare "r" with "y" ?
I can't find any function for this type of compare , and the evalXXXX function does NOT have another input for this situation .