Collaborative filtering is a branch of recommendation that takes account of the information about different users. The word "collaborative" refers to the fact that users collaborate with each other to recommend items. In fact, the algorithms take account of user purchases and preferences. The starting point is a rating matrix in which rows correspond to users and columns correspond to items.
This section will show you an example of item-based collaborative filtering. Given a new user, the algorithm considers the user's purchases and recommends similar items. The core algorithm is based on these steps:
For each two items, measure how similar they are in terms of having received similar ratings by similar users
For each item, identify the k-most similar items
For each user, identify the items that are most similar to the user's purchases
In this chapter, we will see the overall approach to building an IBCF model. In addition, the upcoming sections will show its...
