Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 55

The most basic question comes in mind that what is recommender system. A recommender system is a software or model that analyze a client’s preference, and based on that, it generates a list of items for that client. A multi-criteria recommender system can be defined as recommender systems that collect information on multiple criteria. The basic working of recommender system is to predict accurate recommendation for a particular user. The recommender system or single-criteria recommender system explores only one criteria and give recommended result. This is the first ever recommender system concept. But for real-world problems, we cannot predict recommendation list by exploring only one criterion at a time. It will give false prediction. So, the MCRS concept comes in the field. These kinds of recommender system can explore multiple-criteria at a time and can give excellent accuracy (Figure 3.1).

Figure 3.1 Working principle of MCRS.

Figure 3.2 Phases of MCRS.

Recommender systems are widely used in e-commerce systems and movie industries and each and every sector. Suppose if we used amazon. com and buy a product, then before check out it shows similar kind of product as add on. This list of items is predicted by amazons very own recommender system. Similarly, if we use Netflix, then we can see that it always recommends new movies and web series to us. This prediction is based on generally two categories, on the basis of our previous choice and other one is on the basis of out Netflix account details. That is how recommender system generates a list which is most suitable to the user.

Every recommender system goes through three types of phases. Those phases are modeling phase, prediction phase, and recommendation phase.

Figure 3.2 explains the different phases of a recommender system. Now, we will see the each phases and their significance.