Collaborative filtering is a type of recommendation system that uses the past behavior or preferences of users to make recommendations. This is in contrast to content-based filtering, which uses the characteristics or content of items to make recommendations. Collaborative filtering algorithms work by analyzing the past behavior of users to identify patterns and similarities. For example, if two users have both purchased the same items or given high ratings to the same items, the algorithm might conclude that they have similar tastes and recommend items that one user has purchased or rated highly to the other user. In this way, the algorithm can make personalized recommendations to each user based on the collective wisdom of the entire user community.
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