R List Save as Quoted List

Introduction to RecommenderLab and RStudio

RecommenderLab is a popular R package used for building recommender systems. It provides an efficient way to train, evaluate, and deploy recommender models using various algorithms, including Matrix Factorization (MF), Collaborative Filtering (CF), and Hybrid models. In this article, we’ll explore how to save the output of RecommenderLab as a quoted list in R.

The Problem

When working with RecommenderLab, it’s common to need to extract the predicted movie recommendations for a given user from the model’s output. However, when using the recommender package, the recommended movies are returned as a vector with quotes around each title. To make this data more useful, we want to save these recommendations in a format that allows us to easily access and manipulate them.

The Current Solution

One possible approach is to use the cat() function to concatenate the quoted movie titles into a single string, separated by commas. This can be achieved using the following code:

library("recommenderlab")
library("stringi")

data("MovieLense")
MovieLense100 <- MovieLense[rowCounts(MovieLense) > 100,]
train <- MovieLense100[1:50]
rec <- Recommender(train, method = "UBCF")
pre <- predict(rec, MovieLense100[101:105], n = 10)

list1 <- as(pre, "list")

cat(paste0(shQuote(list1[["291"]]), collapse=","))

This code extracts the predicted movie recommendations for a specific user (user 291) and converts them into a quoted string. However, this approach has limitations. For example, it only works for single quotes of movies, and it doesn’t provide any structure or organization to the data.

Alternative Solution: Using DataFrames

Another approach is to save each movie as a separate column in a dataframe, making it easier to access and manipulate the data without having to parse the quoted strings multiple times. We can achieve this using the tidyr and dplyr packages.

library("recommenderlab")
library("tidyr")
library("dplyr")

data("MovieLense")
MovieLense100 <- MovieLense[rowCounts(MovieLense) > 100,]
train <- MovieLense100[1:50]
rec <- Recommender(train, method = "UBCF")
pre <- predict(rec, MovieLense100[101:105], n = 10)

recommendedMovies <- c("Titanic (1997)", "Contact (1997)", "Alien (1979)", "Amadeus (1984)", 
                        "Godfather, The (1972)", "Aliens (1986)", "Sting, The (1973)", "American Werewolf in London, An (1981)",
                        "Schindler's List (1993)", "Glory (1989)")

theData <- data.frame(name = "Santhosh",
                      rank = 1:length(recommendedMovies),
                      movies = recommendedMovies,
                      stringsAsFactors = FALSE)

theData %>% group_by(name) %>% 
  spread(., rank, movies, sep = "movie")

This code creates a dataframe recommendedMovies that contains the quoted movie titles as separate rows. Then it uses the spread() function from tidyr to pivot this data into a long format with two additional columns: rank and name. The resulting output is:

As we can see, this approach provides a much more structured and organized way of accessing the movie recommendations.

Conclusion

In this article, we explored how to save RecommenderLab’s predicted movie recommendations as a quoted list in R. We discussed two possible approaches: concatenating the quoted titles using cat() or creating a dataframe with separate columns for each movie recommendation. The second approach using dataframes is more flexible and easier to manipulate, making it a better choice for many use cases.

By following the code examples provided in this article, you can easily save your own RecommenderLab predictions as a quoted list or dataframe, depending on your specific needs and requirements.

Last modified on 2024-07-24