Collaborative filtering

Collaborative filtering is a method used in recommender systems to make personalized recommendations to users. It is based on the idea of using the ratings or preferences of users to identify items that are likely to be of interest to other users.

In collaborative filtering, a recommender system tries to identify users who have similar tastes or preferences, and uses the ratings or preferences of these users to make recommendations to a target user. This is done by calculating the similarity between users based on their ratings or preferences, and using this similarity to predict the rating or preference that a target user would give to a particular item.

There are two main types of collaborative filtering: user-based collaborative filtering and item-based collaborative filtering.

User-based collaborative filtering involves comparing the ratings or preferences of a target user to those of other users in the system, and using the similarities between these users to make recommendations. For example, if a target user and another user both have high ratings for a particular set of movies, the system might recommend movies that the other user has rated highly but that the target user has not yet seen.

Item-based collaborative filtering involves comparing the ratings or preferences that different users have given to a particular item, and using this information to make recommendations to a target user. For example, if a target user has rated a particular movie highly, the system might recommend other movies that have been rated highly by other users who also rated the first movie highly.

Collaborative filtering can be used to make recommendations in a variety of contexts, including e-commerce, streaming platforms, and social media. It is a popular method for making personalized recommendations because it does not require explicit feedback or ratings from users, and can be used to make recommendations based on the implicit feedback that users provide through their actions or interactions with a system or platform.

Provide an answer from a number of possibilities. Recognize and state briefly a distinguishing fact or feature.

Give an expected result of an upcoming action or event.