
Collaborative filtering
Algorithm used by recommender systems

Algorithm used by recommender systems
Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about a user's interests by utilizing preferences or taste information collected from many users (collaborating). This approach assumes that if persons A and B share similar opinions on one issue, they are more likely to agree on other issues compared to a random pairing of A with another person. For instance, a collaborative filtering system for television programming could predict which shows a user might enjoy based on a limited list of the user's tastes (likes or dislikes). These predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example...
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