Page "Collaborative filtering" Paragraph 1

In the newer, narrower sense, collaborative filtering is a method of making automatic predictions ( filtering ) about the interests of a user by collecting preferences or taste information from many users ( collaborating ).

The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue x than to have the opinion on x of a person chosen randomly.

For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes ( likes or dislikes ).

Note that 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 based on its number of votes.