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.


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Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…