A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item.[1][2]
Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts,collaborators, jokes, restaurants, garments, financial services, life insurance, romantic partners (online dating), and Twitter pages
Content based systems, recommends item based on a similarity comparison between the content of the items and a user’s profile. The feature of items are mapped with feature of users in order to obtain user – item similarity.
Content-based recommendation lacks in detecting inter dependencies or complex behaviors. For example: People might like smartphones with Good Display, only if it has retina display and wouldn’t otherwise.
Collaborative Filtering algorithm considers “User Behaviour” for recommending items. They exploit behaviour of other users and items in terms of transaction history, ratings, selection and purchase information. Other users behaviour and preferences over the items are used to recommend items to the new users. In this case, features of the items are not known.