The complete R code is shown as follows:
library(data.table) wine.data <- fread('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data') head(wine.data) table(wine.data$V1) wine.type <- wine.data[,1] wine.features <- wine.data[,-1] wine.features.scaled <- data.frame(scale(wine.features)) wine.mat <- data.matrix(wine.features.scaled) rownames(wine.mat) <- seq(1:dim(wine.features.scaled)[1]) wine.mat[1:2,] wine.mat <- t(wine.mat) cor.matrix <- cor(wine.mat, use = "pairwise.complete.obs", method = "Pearson") dim(cor.matrix) cor.matrix[1:5,1:5] user.view <- wine.features.scaled[3,] user.view sim.items <- cor.matrix[3,] sim.items sim.items.sorted <- sort(sim.items, decreasing = TRUE) sim.items.sorted[1:5] rbind(wine.data[3,] ,wine.data[52,] ,wine.data[51,] ,wine.data[85,] ,wine.data[15,] ) library(tidyverse) library(tidytext) library(tm) library(slam) cnames <- c('ID' , 'TITLE' , 'URL' , 'PUBLISHER...