The rapid progress in science and technology in recent years has led to the creation of large amounts of data about users. A huge amount of structured, semi-structured and unstructured data is generated very quickly every day, such as the comments users write for products on the internet, the ratings they give to products, feedback or shopping details.
Recommendation systems analyze this data coming from heterogeneous sources belonging to different users and generate solutions to predict the interests of the users and accordingly recommend the relevant products to the right users. Recommendation systems, which are also seen as filtering unnecessary and unwanted information, are one of the most powerful machine learning methods. Although it is used extensively especially in online platforms, there are widespread applications to predict the next behavior of users, not only in online applications, but also in all kinds of platforms where users interact with a system. Today, deep learning, data mining, machine learning and big data techniques are benefited to develop recommendation models.