The basic idea of these systems is that, if two users share the same interests in the past, that is, they liked the same book, they will also have similar tastes in the future. If, for example, user A and user B have a similar purchase history and user A recently bought a book that user B has not yet seen, the basic idea is to propose this book to user B. The book recommendations on Amazon are one good example of this type of recommender system.
In this type of recommendation, filtering items from a large set of alternatives is done collaboratively between users preferences. Such systems are called collaborative filtering recommender systems.
While dealing with collaborative filtering recommender systems, we will learn about the following aspects:
How to calculate the similarity between users
How to calculate the similarity between items
How do we deal with new items and new users whose data is not known
The collaborative filtering approach considers only user preferences and does not take into account the features or contents of the items being recommended. This approach requires a large set of user preferences for more accurate results.
