Hybrid Recommender Systems are computer systems that combine different types of information and predictive algorithms to generate recommended items for customers. They are used in many online commerce and information services, such as Netflix, Amazon, and Spotify, to serve customers with more informed recommendations.
Hybrid Recommender Systems typically use two or more methods including Collaborative filtering, Content-based filtering, and Knowledge-based systems. The use of a combination of techniques allows the system to overcome certain deficiencies of single methods and produce better recommendations than individual techniques by themselves.
In Collaborative filtering, a database containing the preferences of individual users is built from feedback provided by the users. Items that are either liked or disliked by the users are used to build the database and to predict the preferences of other users who have similar tastes.
Content-based filtering recommends an item to a user based on the similarity of the item to other items the user has previously liked or used. The method uses key features or words associated with the items to make the comparison.
In Knowledge-based systems, a knowledge base is filled with facts about the items, such as item types, attributes, and ratings, and artificial intelligence is used to match the user’s preferences with the items in the knowledge base.
Hybrid Recommender Systems are used in many industries, ranging from e-commerce to health care, where they are used to recommend relevant items or services to customers. The use of multiple techniques in one system provides more accurate and personalized recommendations for individual users.
