Netflix Recommendation System
Netflix offers a large number of shows and movies for streaming with its recommendation system. Netflix energized research in recommender systems with its Netflix Prize competition in 2006, since then recommender systems have come a long way and have been integrated in many types of businesses.
Content-based filtering is commonly used for recommender systems. It treats recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features. In this system, it tries to recommend items that are similar to those that a user liked in the past, or is examining in the present and the best-matching items are recommended. This uses information retrieval and information filtering.
Generally a user profile is created and the system mostly focuses on two types of information: A model of the user's preference and a history of the user's interaction with the recommender system.
Basically, an item profile is used (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied.
