The goal of a recommender system is to generate relevant recommendations for users. It is an information filtering technique that assists users by filtering the redundant and unwanted data from a data chunk and delivers relevant information to the users. An information system is known as recommendation engine when the delivered information comes in the form of suggestions. Since different users have different interests, the information filtering system must be personalized to accommodate the individual users interests. This requires gathering of feedbacks from the user in order to make a user profile of his preferences. Recommender systems have become extremely common in the recent years, and are applied in a variety of fields. Usually recommender systems are based on the keyword search which allows the efficient scanning of very large document collections. Recommender systems typically produce a list of recommendations through collaborative or content-based filtering techniques.Different recommendation systems are available for different scenarios. In this paper, a detailed review of various recommendation systems is presented.

Collaboration Content based Information filtering