Collaborative filtering is based solely on user ratings or other user behaviors, making recommendations based on what users with similar behaviors liked or purchased.

A key advantage of collaborative filtering is that it does not rely on item content, and therefore, it is capable of accurately recommending complex items, such as movies, without understanding the item itself. The underlying assumption is that people that agreed in the past will agree in the future, and that they will like similar kinds of items to what they liked in the past.

A major disadvantage of this approach is the so-called cold start, meaning that if we want to build an accurate collaborative filtering system, the algorithm often needs a large amount of user ratings. This usually takes collaborative filtering out of the first version of the product, and it is introduced later, when a decent amount of data has been collected.