如何使用交叉验证评估矩阵分解推荐系统的准确性？

如何解决如何使用交叉验证评估矩阵分解推荐系统的准确性？

我想使用交叉验证来访问我的模型，但是我无法将其放入此代码中。

accuracy <- function(x,real_ratings){
#user and book factors
#first 100 elements are latent factors for users and 150 elements for books
user_factors <- matrix(x[1:100],50,10)
book_factors <- matrix(x[101:250],10,100)
#The user-book:- interactionpredictions for users and book factors from dot products
predictions <- user_factors %*% book_factors
#sum of squared errors of over all rated books
errors <- (real_ratings - predictions) ^ 2
sqrt(mean(errors[!is.na(real_ratings)]))
}
set.seed(123)
# optimization step
rec <- optim(par = runif(280),accuracy,real_ratings = df,control = list(maxit = 100000000))
rec$convergence
rec$value
# extract optimal user factors
user_factors <- matrix(rec$par[1:100],10)
# extract optimal movie factors
book_factors <- matrix(rec$par[101:250],100)
head(book_factors)
set.seed(123)
# check predictions for one user
predicted_ratings <- user_factors %*% book_factors
rbind(round(predicted_ratings[1,],1),as.numeric(df[1,]))
#check accuracy
errors <- (df - predicted_ratings)^2
sqrt(mean(errors[!is.na(df)]))

我不知道该怎么办。