1:

predict.knnMF <- function(recoOut,newCase,k=5)
{
   W <- recoOut$P
   H <- t(recoOut$Q)
   H[is.nan(H)] <- 0
   userID <- newCase[1]
   itemID <- newCase[2]
   nrw <- nrow(W)
   cosines <- vector(length = nrw)
   for (i in 1:nrw) {
      cosines[i] <- cosdist(W[userID,],W[i,])
   }
   nearby <- order(cosines,decreasing=TRUE)[1:k]
   preds <- W[nearby,] %*% H[,itemID]
   mean(preds)
}

cosdist <- function(x,y) 
{
   x %*% y / (l2a(x) * l2a(y))
}

l2a <- function(x) sqrt(x %*% x)

library(rectools)
load('~/ML100K')
ud <- udata[,1:3]
set.seed(9999)  # important!
z <- trainReco(ud,nmf=T)
print(predict.knnMF(z,c(5,12)))