This mechanism uses user rating dat

This mechanism uses user rating data to compute similarity between users or items. This is used for making recommendations. This was the earlier mechanism and is used in many commercial systems. It is easy to implement and is effective. Typical examples of this mechanism are neighbourhood based CF and item-based/user-based top-N recommendations.[3] For example, in user based approaches, the value of ratings user 'u' gives to item 'i' is calculated as an aggregation of some similar users rating to the item:

where 'U' denotes the set of top 'N' users that are most similar to user 'u' who rated item 'i'. Some examples of the aggregation function includes:

where k is a normalizing factor defined as k =1/sum_{u^prime in U}|operatorname{simil}(u,u^prime)| . and ar{r_u} is the average rating of user u for all the items rated by that user.