This section will show you how to build a recommendation model using item descriptions and user purchases. The model combines item-based collaborative filtering with some information about the items. We will include the item description using a monolithic hybrid system with feature combination. The recommender will learn from the two data sources in two separate stages.

Following the approach described in Chapter 3, *Recommender Systems*, let's split the data into the training and the test set:

which_train <- sample(x = c(TRUE, FALSE),size = nrow(ratings_matrix),replace = TRUE,prob = c(0.8, 0.2))recc_data_train <- ratings_matrix[which_train, ]recc_data_test <- ratings_matrix[!which_train, ]

Now, we can build an IBCF model using `Recommender`

. Since the rating matrix is binary, we will set the distance method to `Jaccard`

. For more details, look at the *Collaborative filtering on binary data* section in Chapter 3, *Recommender Systems*. The remaining parameters are left...