The best approach
In this section, we are trying to build the best possible recommendation engine. There are two parts to this section:
Understanding the key concepts
Implementing the best approach
Our first part covers the basic concepts, such as how the CF and KNN algorithms work, what kind of features we need to choose, and so on. In the second part, we will be implementing the recommendation engine using the KNN and CF algorithm. We will generate the accuracy score as well as the recommendation for books. So let's begin!
Understanding the key concepts
In this section, we will understand the concepts of collaborative filtering. This covers a lot of aspects of the recommendation system. So, let's explore CF.
Collaborative filtering
There are two main types of collaborative filtering, as follows:
Memory-based CF:
User-user collaborative filtering
Item-item collaborative filtering
Model-based CF:
Matrix-factorization-based algorithms
Deep learning
We will begin with memory-based CF and then move on to...
