R Deep Learning Cookbook

How to do it...

This recipe covers the steps for setting up collaborative filtering using an RBM.

1. Read the movies.dat datasets in R:

txt <- readLines("movies.dat", encoding = "latin1") txt_split <- lapply(strsplit(txt, "::"), function(x) as.data.frame(t(x), stringsAsFactors=FALSE)) movies_df <- do.call(rbind, txt_split) names(movies_df) <- c("MovieID", "Title", "Genres") movies_df$MovieID <- as.numeric(movies_df$MovieID) 

2. Add a new column (id_order) to the movies dataset, as the current ID column (UserID) cannot be used to index movies because they range from 1 to 3,952:

movies_df$id_order <- 1:nrow(movies_df) 

3. Read the ratings.dat dataset in R:

ratings_df <- read.table("ratings.dat", sep=":",header=FALSE,stringsAsFactors = F)