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