Nowadays, there are few people who choose books on the advice of their acquaintances. Most people use recommendation systems on specialized sites and, of course, read reviews.
Book recommendation systems have revolutionized the way we consume literature. These systems help readers find books that match their interests and preferences, providing endless possibilities to expand one’s literary horizons.
The primary advantage of book recommendation systems is personalization. They offer tailored recommendations based on individual reading habits, genres, authors, and other factors such as past ratings or reviews. This feature saves time and effort in searching for books manually while enhancing the overall reading experience. Additionally, it helps publishers and authors reach out to targeted audiences by promoting their new releases or lesser-known titles to interested readers.
Each recommendation system is a unique algorithm. However, they also have a lot in common. For example, almost all systems evaluate the similarity of book content. In this post I will show you a code example of one of these systems in Python.
