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Recommender systems typically produce a list of recommendations in one of two ways-through collaborative or content-based filtering.

Collaborative filtering approaches to build a model from a user's past behavior ( items previously purchased or selected and / or numerical ratings given to those items ) as well as similar decisions made by other users, then use that model to predict items ( or ratings for items ) that the user may have an interest in.

Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.

These approaches are often combined ( see Hybrid Recommender Systems ).