gautamraghwani16/book-recommendation-system

Book-Recommendation-system

Types Of Recommendation Systems Machine learning solves many problems but making product recommendations is a widely known application of machine learning. There are three main types of recommendation systems –

1. Collaborative Filtering

The collaborative filtering method is based on gathering and analyzing data on user’s behavior. This includes the user’s online activities and predicting what they will like based on the similarity with other users.

For example, if user A likes Apple, Banana, and Mango while user B likes Apple, Banana, and Jackfruit, they have similar interests. So, it is highly likely that A would like Jackfruit and B would enjoy Mango. This is how collaborative filtering takes place.

2. Content-Based Filtering

Content-based filtering methods are based on the description of a product and a profile of the user’s preferred choices. In this recommendation system, products are described using keywords, and a user profile is built to express the kind of item this user likes.

For instance, if a user likes to watch movies such as Iron Man, the recommender system recommends movies of the superhero genre or films describing Tony Stark.

The central assumption of content-based filtering is that you will also like a similar item if you like a particular item.