Collaborative filtering (CF) is a core method used by recommender systems to filter suggestions by collecting and analyzing preferences about other similar users. CF techniques use available information and preference pattern data to make predictions (filters) about a particular user's interests.
The collaborative aspect of CF is associated with the notion that relevant recommendations are derived from other user preferences. CF also assumes that two individuals with similar preferences are more likely to share preferences for a particular item than two other individuals selected at random. Accordingly, the primary task of CF is to generate item suggestions (predictions) based on other (collaborative) similar users within the system.
To identify similar users and find ratings (preferences) of unrated items, recommender...