ibcf_recc.R

library(recommenderlab)

#Convert data.frame in to transactions:
#Convert to binaryRatingMatrix:

data_train <- as(data_train, "transactions") 
data_train_1 <- as(data_train, "binaryRatingMatrix")


data_test<- as(data_test, "transactions") 
data_test_1 <- as(data_test, "binaryRatingMatrix")
# Find top 10 recomm movies with Item based collab filter

model1 <- Recommender(data = movies3, method = "IBCF", parameter = list(k = 25, 
                                                                             method = "pearson"))
model1

# Applying model to test

predicted1 <- predict(object = model1, newdata = data_test_1, n = 10)
predicted1


# The latest among those predicted for each user as most recommended


reccom <- data.frame(user_id= sort(rep(1:length(predicted1@items))),
      rating = unlist(predicted1@ratings), movie_id = unlist(predicted1@items))

#Displaying the recommendations for first 25 users


reccom_list<- reccom[order(reccom$user_id),] 
head(reccom_list,25)