inst/recommendMovies2.R

In paulij/recommendMovies: Recommend Movies

library(rjson)

jsonString <- "[[126,5], [156,3], [566,2], [1333,4], [1660,5],
 [2126,3], [3126,2], [4126,5], [5126,3], [6126,1], [7126,5], [8000,2], [8007,3],
 [8100,3], [8200,4], [8300,5], [8310,4], [8320,5], [8330,1]]"

rjsonData <- fromJSON(jsonString)
frame  <- as.data.frame(rjsonData)
matrix <- as.matrix(frame)
matrix

movieIds <- matrix[1,]
ratings <- matrix[2,]
numberMovieIds <- length(movieIds)
numberMovieIds

i <- rep.int(1, numberMovieIds)

sparseMatrix <- sparseMatrix(i, movieIds, x = ratings, dims=c(1,8552))
r <- new("realRatingMatrix", data = sparseMatrix)
summary(r)
str(r)

load("data/recommender.rda")

# make a topN prediction for user1 and N = 10
user1topN <- predict(recommender, r, n = 10)
user1topN@items[[1]]

## now get the movie names from movies.csv
movies <- read.csv("small/movies.csv")

## have a look at some data
?subset
subset.data.frame(movies, movieId < 10)

## lets have a quick look at %in%
1 %in% c(1,2)
subset.data.frame(movies, movieId %in% c(1,2))

expression(330 %in% user1topN@items[[1]])

## recommended movies for user1
user1topN@items[[1]]
subset.data.frame(movies, movieId %in% user1topN@items[[1]])