Create Sparse Matrix from a data frame

问题:

I m doing an assignment where I am trying to build a collaborative filtering model for the Netflix prize data. The data that I am using is in a CSV file which I easily imported into a data frame. Now what I need to do is create a sparse matrix consisting of the Users as the rows and Movies as the columns and each cell is filled up by the corresponding rating value. When I try to map out the values in the data frame I need to run a loop for each row in the data frame, which is taking a lot of time in R, please can anyone suggest a better approach. Here is the sample code and data:

buildUserMovieMatrix <- function(trainingData)
{
  UIMatrix <- Matrix(0, nrow = max(trainingData$UserID), ncol = max(trainingData$MovieID), sparse = T);
  for(i in 1:nrow(trainingData))
  {
    UIMatrix[trainingData$UserID[i], trainingData$MovieID[i]] = trainingData$Rating[i];
  }
  return(UIMatrix);
}

Sample of data in the dataframe from which the sparse matrix is being created:

    MovieID UserID  Rating
1       1      2       3
2       2      3       3
3       2      4       4
4       2      6       3
5       2      7       3

So in the end I want something like this: The columns are the movie IDs and the rows are the user IDs

    1   2   3   4   5   6   7
1   0   0   0   0   0   0   0
2   3   0   0   0   0   0   0
3   0   3   0   0   0   0   0
4   0   4   0   0   0   0   0
5   0   0   0   0   0   0   0
6   0   3   0   0   0   0   0
7   0   3   0   0   0   0   0

So the interpretation is something like this: user 2 rated movie 1 as 3 star, user 3 rated the movie 2 as 3 star and so on for the other users and movies. There are about 8500000 rows in my data frame for which my code takes just about 30-45 mins to create this user item matrix, i would like to get any suggestions

回答1:

The Matrix package has a constructor made especially for your type of data:

library(Matrix)
UIMatrix <- sparseMatrix(i = trainingData$UserID,
                         j = trainingData$MovieID,
                         x = trainingData$Rating)

---

Otherwise, you might like knowing about that cool feature of the [ function known as matrix indexing. Your could have tried:

buildUserMovieMatrix <- function(trainingData) {
  UIMatrix <- Matrix(0, nrow = max(trainingData$UserID),
                        ncol = max(trainingData$MovieID), sparse = TRUE);
  UIMatrix[cbind(trainingData$UserID,
                 trainingData$MovieID)] <- trainingData$Rating;
  return(UIMatrix);
}

(but I would definitely recommend the sparseMatrix approach over this.)

回答2:

This will probably be faster than a loop.

library(reshape2)
m <- dcast(df,UserID~MovieID,fill=0)[-1]
m
#   1 2
# 1 3 0
# 2 0 3
# 3 0 4
# 4 0 3
# 5 0 3

If you use data.tables, it will be a lot faster:

library(data.table)
DT <- as.data.table(df)
m  <- dcast(DT,UserID~MovieID,fill=0)[-1]

And as I'm sure someone will point out, you can use this instead

setDT(df)
m  <- dcast(df,UserID~MovieID,fill=0)[-1]

This converts df to a data.table in place (without making a copy). if your data set is enormous, that can make a difference...