Recommendation systems
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 or RE) is a subclass of information filtering systems that help to predict the rating or preference, based on the rating provided by users for an item. In recent years, recommendation systems have become increasingly popular.
For example, at Amazon, the importance of suggesting the right item to the right user can be gauged by the fact that 35% of all sales are estimated to be generated by the recommendation...
