Collaborative filtering (CF) is the method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of CF approach is that: Those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommender system for music tastes could make predictions about which music 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 more simple approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes.
Collaborative Filtering systems usually take two steps: 1.Looking for users who share the same rating patterns with the active user (the user who the prediction is for). 2.Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user… more into the subject