Approaches to building recommender systems

Recommender systems aim to suggest relevant products to buyers. Because of their ability to consider the uniqueness of buyers, intelligent recommender engines have generated billions of dollars for businesses and helped buyers find relevant products. They represent a win-win for both consumers and businesses. Various data-driven approaches to creating intelligent recommendation systems have been introduced. There are three major approaches to recommendation systems: collaborative filtering systems, content-based systems, and hybrid systems. Let's discuss each of these approaches in the following sub-sections.

Collaborative filtering recommender systems

The core idea behind collaborative filtering recommender systems is leveraging past actions by others to infer what an individual might be interested in. Collaborative filtering approaches draw on data stores of the historic interaction between products and users. Table 10.1 presents...