Recommender system

There is a lot of interest in recommending items to a user. Suppose a user goes to an e-commerce site, what should be recommended to the user? Items might be recommended based upon what a user previously liked, bought, or what their friends liked. Recommenders deal with discovering new items for which a user could have a higher preference.

Recommendations are computed using two major paradigms, collaborative and content based filtering. Collaborative filtering works on recommendations based upon user’s past behaviour and behaviour of user’s similar to the current user. Items based upon other similar items purchased are also taken into consideration. Content based filtering uses the characteristic of the item to be recommended to produce the recommendations. Let’s assume that we need to recommend books. Collaborative filtering approach will recommend books based upon purchase history of similar users and similar books. Content based recommenders will...