Collaborative Filtering

What Does Collaborative Filtering Mean?

Collaborative filtering (CF) is a technique commonly used to build personalized recommendations on the Web. Some popular websites that make use of the collaborative filtering technology include Amazon, Netflix, iTunes, IMDB, LastFM, Delicious and StumbleUpon. In collaborative filtering, algorithms are used to make automatic predictions about a user’s interests by compiling preferences from several users.

Techopedia Explains Collaborative Filtering

For example, a site like Amazon may recommend that the customers who purchase books A and B purchase book C as well. This is done by comparing the historical preferences of those who have purchased the same books.

Different types of collaborative filtering are as follows:

• Memory Based: This method makes use of user rating information to calculate the likeness between the users or items. This calculated likeness is then used to make recommendations.
• Model Based: Models are created by using data mining, and the system learns algorithms to look for habits according to training data. These models are then used to come up with predictions for actual data.
• Hybrid: Various programs combine the model-based and memory-based CF algorithms.