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.
