Recommender techniques are nothing but information agents that try to predict items that users may be interested in and recommend the best ones to the target user. These techniques can be classified based on the information sources they use. For example, user features (age, gender, income, and location), item features (keywords, and genres), user-item ratings (explicit ratings, and transaction data), and other information about the user and item that are useful for the process of recommendation.
Thus, a recommendation system; otherwise known as a recommendation engine (RE) is a subclass of information filtering systems that help to predict the rating or preference based on the rating provided by users to an item. In recent years, recommendation systems have become increasingly popular.
