Cluster models are another often-used approach to making a functioning recommendation system, according to an article by three Amazon insiders. Cluster models divide users into groups, or clusters, with the most similar users. The clusters are generated and each assigned a random user at first, and then other users are compared and classified.
The issue with cluster models is that grouping users into these clusters doesn't mean that they are grouped with the most similar other users. This can lead to recommendations of less quality.