Before rolling out the recommender system to the users, how do we ensure that the system is efficient or accurate? What is the base on which we state that the system is good? As stated earlier, the goal of any recommendation system is to recommend more relevant and useful items to the user. A lot of research has been happening in developing new methods to evaluate the recommender systems to improve the accuracy of the systems.

In Chapter 4, Evaluating the Recommender Systems, we will learn about the different evaluation metrics employed to evaluate the recommender systems, these include setting up the evaluation, evaluating recommender systems, optimizing the parameters. This chapter also focuses on how important evaluating the system is during the design and development phases of building recommender systems and the guidelines to be followed in selecting an algorithm based on the available information about the items and the problem statement. This chapter also covers the different experimental setups in which recommender systems are evaluated.