Collaborative Filtering
( Collaborative Filtering
) recommendation algorithm is the most classic and most commonly used recommendation algorithm.
The so-called collaborative filtering, the basic idea is
to recommend items to users
based on their previous preferences and the choices of other users with similar interests
(
based on user historical behavior
Data mining discovers the user’s preference bias, and predicts the products that the user may like for recommendation
)
,
generally based only on user behavior data (evaluation, purchase
Buy, download, etc.)
,
without any additional information (items own characteristics) depends on the item or user of any additional information (age, gender, etc.)
. Current application
The more extensive collaborative filtering algorithm is a neighborhood-based method, and this method mainly has the following two algorithms:
1.
User-based collaborative filtering algorithm
(UserCF)
:
Recommend products that are liked by other users with similar interests
2.
Item-based collaborative filtering algorithm
(ItemCF)
:
Recommend items that are similar to the items he liked before