Collaborative-filtering-for-Music-recommendation/Script.R at master · geeky-bit/Collaborative-filtering-for-Music-recommendation · GitHub

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  options(digits=4)
  # I am using only the data from Germany for simplicity
  # but you can go ahead and use the entire dataset
  #data <- read.csv(file="lastfm-data.csv")
  data.germany <- read.csv(file="lastfm-matrix-germany.csv")

############################
#  Item Based Similarity   #
############################

# Drop the user column and make a new data frame
  data.germany.ibs <- (data.germany[,!(names(data.germany) %in% c("user"))])

# Create a helper function to calculate the cosine between two vectors
  getCosine <- function(x,y)
  {
    this.cosine <- sum(x*y) / (sqrt(sum(x*x)) * sqrt(sum(y*y)))
    return(this.cosine)
  }

# Create a placeholder dataframe listing item vs. item
  holder <- matrix(NA, nrow=ncol(data.germany.ibs),ncol=ncol(data.germany.ibs),dimnames=list(colnames(data.germany.ibs),colnames(data.germany.ibs)))
  data.germany.ibs.similarity <- as.data.frame(holder)

# Lets fill in those empty spaces with cosine similarities
  for(i in 1:ncol(data.germany.ibs)) {
    for(j in 1:ncol(data.germany.ibs)) {
      data.germany.ibs.similarity[i,j]= getCosine(data.germany.ibs[i],data.germany.ibs[j])
    }
  }

# Output similarity results to a file
  write.csv(data.germany.ibs.similarity,file="final-germany-similarity.csv")

# Get the top 10 neighbours for each
  data.germany.neighbours <- matrix(NA, nrow=ncol(data.germany.ibs.similarity),ncol=11,dimnames=list(colnames(data.germany.ibs.similarity)))

  for(i in 1:ncol(data.germany.ibs))
  {
    data.germany.neighbours[i,] <- (t(head(n=11,rownames(data.germany.ibs.similarity[order(data.germany.ibs.similarity[,i],decreasing=TRUE),][i]))))
  }

# Output neighbour results to a file
  write.csv(file="final-germany-item-neighbours.csv",x=data.germany.neighbours[,-1])


############################
# User Scores Matrix       #
############################
# Process:
# Choose a product, see if the user purchased a product
# Get the similarities of that product's top 10 neighbours
# Get the purchase record of that user of the top 10 neighbours
# Do the formula: sumproduct(purchaseHistory, similarities)/sum(similarities)

#  helper function to calculate the scores
  getScore <- function(history, similarities)
  {
    x <- sum(history*similarities)/sum(similarities)
    x
  }

# A placeholder matrix
 holder <- matrix(NA, nrow=nrow(data.germany),ncol=ncol(data.germany)-1,dimnames=list((data.germany$user),colnames(data.germany[-1])))

# Loop through the users (rows)
   for(i in 1:nrow(holder))
   {
       # Loops through the products (columns)
       for(j in 1:ncol(holder))
       {
           # Get the user's name and th product's name
           # We do this not to conform with vectors sorted differently
             user <- rownames(holder)[i]
             product <- colnames(holder)[j]

           # We do not want to recommend products you have already consumed
           # If you have already consumed it, we store an empty string
             if(as.integer(data.germany[data.germany$user==user,product]) == 1)
             {
                 holder[i,j]<-""
              } else {

           # We first have to get a product's top 10 neighbours sorted by similarity
             topN<-((head(n=11,(data.germany.ibs.similarity[order(data.germany.ibs.similarity[,product],decreasing=TRUE),][product]))))
             topN.names <- as.character(rownames(topN))
             topN.similarities <- as.numeric(topN[,1])

           # Drop the first one because it will always be the same song
             topN.similarities<-topN.similarities[-1]
             topN.names<-topN.names[-1]

           # We then get the user's purchase history for those 10 items
             topN.purchases<- data.germany[,c("user",topN.names)]
             topN.userPurchases<-topN.purchases[topN.purchases$user==user,]
             topN.userPurchases <- as.numeric(topN.userPurchases[!(names(topN.userPurchases) %in% c("user"))])

            # We then calculate the score for that product and that user
             holder[i,j]<-getScore(similarities=topN.similarities,history=topN.userPurchases)

         } # close else statement
       } # end product for loop
   } # end user for loop

# Output the results to a file
data.germany.user.scores <- holder
write.csv(file="final-user-scores.csv",data.germany.user.scores)

# Lets make our recommendations pretty
  data.germany.user.scores.holder <- matrix(NA, nrow=nrow(data.germany.user.scores),ncol=100,dimnames=list(rownames(data.germany.user.scores)))
  for(i in 1:nrow(data.germany.user.scores))
  {
    data.germany.user.scores.holder[i,] <- names(head(n=100,(data.germany.user.scores[,order(data.germany.user.scores[i,],decreasing=TRUE)])[i,]))
  }

# Write output to file
  write.csv(file="final-user-recommendations.csv",data.germany.user.scores.holder)