Create item-based recommendations using a co-occurrence matrix

recommendation_engine_example.R

get_recommendation_ratings <- function(rating_file_path) {
  # Read user ID, item ID, user preference CSV data
  ratings <- read.csv(file = rating_file_path, header = FALSE, col.names = c('user', 'item', 'preference'))
  
  # Create item co-occurrence matrix
  co_occurrence_matrix <- crossprod(table(ratings[, c('user', 'item')]))
  
  # Convert long format to wide format and replace NAs with 0s
  user_ratings <- tidyr::spread(ratings, user, preference, fill = 0)
  
  # Remove item ID column and convert data frame to matrix
  user_rating_matrix <- data.matrix(user_ratings[-1])
  
  # Create ratings
  recommendation_rating_matrix <- co_occurrence_matrix %*% user_rating_matrix
  
  # Remove ratings for items that user has already rated
  unrated_item_recommendation_rating_matrix <- recommendation_rating_matrix * ifelse(user_rating_matrix > 0, NA, 1)

  data.frame(unrated_item_recommendation_rating_matrix, check.names = FALSE)
}

get_recommendation_ratings('small.csv')