Collaborative filtering

What is collaborative filtering?
Collaborative Filtering (CF) is a technique that is widely used to create personalized recommendations on the web. Some popular websites that use collaborative filtering technology are Amazon, Netflix, iTunes, IMDB, LastFM, Delicious, and StumbleUpon. Collaborative filtering uses algorithms to make automatic predictions about a user's interests by gathering preferences from multiple users.

For example, a website like Amazon may recommend that customers who buy books A and B also buy book C. It does this by comparing the historical preferences of those who bought the same books.

There are the following types of collaborative filtering:

Storage-based: This method uses user rating information to calculate the similarity between the users or items. This calculated similarity is then used to make recommendations.

Model-based: Models are created using data mining and the system learns algorithms to search for habits based on training data. These models are then used to make predictions about actual data.

Hybrid: Different programs combine the model-based and memory-based CF algorithms.