Intuition:
Item based Collaborative Filtering:
Unlike in user based collaborative filtering discussed previously, in itembased collaborative filtering, we consider set of items rated by the user and computes item similarities with the targeted item. Once similar items are found, and then rating for the new item is predicted by taking weighted average of the user’s rating on these similar items.
let's understand with an example:
As an example: consider below dataset, containing users rating to movies. Let us build an algorithm to recommend movies to CHAN.
 Calculating Item similarities
 Predicting the targeted item rating for the targeted User.
This is a critical step; we calculate the similarity between corated items. We use cosine similarity or pearsonsimilarity to compute the similarity between items. The output for step is similarity matrix between Items.
Code snippet:
#step 1: itemsimilarity
calculation corated items are considered and similarity between two items
#are calculated
using cosine similarity
library(lsa)
ratings =
read.csv("Rating Matrix.csv")
x =
ratings[,2:7]
x[is.na(x)]
= 0
item_sim =
cosine(as.matrix(x))
In this most important step, we first predict the items which the user is not rated by making use of the ratings he has made to previously interacted items and the similarity values calculated in the previous step. First we select item to be predicted, in our case “INCEPTION”, we predict the rating for INCEPTION movie by calculating the weighted sum of ratings made to movies similar to INCEPTION. i.e We take the similarity score for each rated movie by CHAN w.r.t INCEPTION and multiply with the corresponding rating and sum up all the for all the rated movies. This final sum is divided by total sum of similarity scores of rated items w.r.t INCEPTION.
Once all the non rated movies are predicted we recommend top N movies to CHAN. Code for Item based collaborative filtering in R:
#data input
ratings =
read.csv("~Rating Matrix.csv")
"step 1: itemsimilarity
calculation\ncorated items are considered and similarity between two
items\nare calculated using cosine similarity"
library(lsa)
x = ratings[,2:7]
x[is.na(x)] = 0
item_sim =
cosine(as.matrix(x))
"Recommending items for chan: since three
movies are not rated\nas a first step we have to predict rating value for each
movie\nin CHANs case we have to first predict values for Titanic,
Inception,Matrix"
rec_itm_for_user = function(userno)
{
#extract all the movies
not rated by CHAN
userRatings = ratings[userno,]
non_rated_movies = list()
rated_movies = list()
for(i in
2:ncol(userRatings)){
if(is.na(userRatings[,i]))
{
non_rated_movies =
c(non_rated_movies,colnames(userRatings)[i])
}
else
{
rated_movies = c(rated_movies,colnames(userRatings)[i])
}
}
non_rated_movies =
unlist(non_rated_movies)
rated_movies =
unlist(rated_movies)
#create weighted
similarity for all the rated movies by CHAN
non_rated_pred_score =
list()
for(j in 1:length(non_rated_movies)){
temp_sum = 0
df =
item_sim[which(rownames(item_sim)==non_rated_movies[j]),]
for(i in
1:length(rated_movies)){
temp_sum = temp_sum+
df[which(names(df)==rated_movies[i])]
}
weight_mat =
df*ratings[userno,2:7]
non_rated_pred_score =
c(non_rated_pred_score,rowSums(weight_mat,na.rm=T)/temp_sum)
}
pred_rat_mat =
as.data.frame(non_rated_pred_score)
names(pred_rat_mat) =
non_rated_movies
for(k in
1:ncol(pred_rat_mat)){
ratings[userno,][which(names(ratings[userno,])
== names(pred_rat_mat)[k])] = pred_rat_mat[1,k]
}
return(ratings[userno,])
}
> rec_itm_for_user(7)
Users Titanic Batman Inception SuperMan Spiderman matrix
7 CHAN 3.085298 4.5 2.940811 4 1 3.170034
Calling above function gives the predicted values not previously seen values for movies Titanic, Inception, Matrix. Now we can sort and recommend the top items.This is all about Collaborative filtering in R, in my upcoming posts I will talk about content based recommender systems in r.
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