Types of Recommendation System

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 Recommendation System: Types


• Collaborative Recommender System :

– It is the most widely implemented and most mature technology that is available in the market.

– Collaborative recommender systems aggregate ratings or recommendations of objects, recognize commonalities between the users on the basis of their ratings and generate new recommendations based on inter-user comparisons.

– The greatest strength of collaborative techniques is that they are completely independent of any machine-readable representation of the objects being recommended and work well for complex objects where variations in taste are responsible for much of the variation in preferences.

– Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future and that they will like similar kinds of objects as they liked in the past.

– Examples: Amazon, YouTube, and Netflix



• Content-based Recommender System :

– It’s mainly classified as an outgrowth and continuation of information filtering research.

– In this system, the objects are mainly defined by their associated features.

– A content-based recommender learns a profile of the new user’s interests based on the features present, in objects the user has rated.

– It’s basically a keyword-specific recommender system here keywords are used to describe the items.

– Thus, in a content-based recommender system the algorithms used are such that it recommends users similar items that the user has liked in the past or is examining currently

– Examples: Social media, youtube



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