A recommendation engine analyzes the general liking or purchase behavior of people and helps in predicting their preferences through similarity computations. Similarity can be computed for the user or item based on the algorithm that we implement.
The recommendation engine can be implemented using the collaborative filtering method, content-based method, or a combination of both these methods. In this chapter, we will see the implementation of the collaborative filtering method in detail.
The collaborative filtering method can be further classified into user- and item-based methods. The user-based collaborative filtering method is a method where we compute the similarity between the users to arrive at the recommendations, whereas, in the case of an item-based similarity method, we compute the similarity between the items.
Recommendation systems are popular across multiple fields. To be specific, let's consider the e-commerce domain, where, based on the purchase...