Chapter 3. Recommender Systems
This chapter shows some popular recommendation techniques. In addition, we will implement some of them in R.
We will deal with the following techniques:
Collaborative filtering: This is the branch of techniques that we will explore in detail. The algorithms are based on information about similar users or similar items. The two sub-branches are as follows:
Item-based collaborative filtering: This recommends to a user the items that are most similar to the user's purchases
User-based collaborative filtering: This recommends to a user the items that are the most preferred by similar users
Content-based filtering: This is for each user; it defines a user profile and identify the items that match it.
Hybrid filtering: This combines different techniques.
Knowledge-based filtering: This is uses explicit knowledge about users and items.
