Collaborative filtering Algorithms Collaborative Filtering Concept Collaborative filtering is a technique used in recommendation systems to predict a user's preferences based on their past behaviour and the behaviour of similar users. This method is predicated on the idea that people who have had similar preferences in the past would continue to have those preferences.  Here is an example of how collaborative filtering works : Suppose we have a dataset of user ratings for different movies. We want to recommend movies to users based on their past ratings. We use collaborative filtering to find similar users and recommend movies that these users have rated highly. Collaborative filtering can be done using two approaches : user-based and item-based. In the user-based approach, the algorithm finds users who have similar ratings and recommends items that these users have rated highly. In the item-based approach, the algorithm finds items that are similar to the ones the user has rated