Introducing the basic concepts of recommender systems
A recommender system is a type of information filtering system that’s designed to suggest items or content to users based on their preferences, historical behavior, or other relevant factors. These systems are widely used in various online platforms to help users discover products, services, content, and more. Recommender systems involve two primary entities: users and items. Users are individuals for whom recommendations are generated, and items are the products, content, or services to be recommended. These items can include movies, books, products, news articles, and more.
Recommender systems rely on data that captures the interaction between users and items. This interaction data can include user ratings, purchase history, clicks, views, likes, and any other form of user engagement with items.
There are different types of recommender systems:
- Collaborative filtering (CF): CF methods make recommendations...
